The Demand-side Grid (dsgrid)
Model Documentation

Electrification Futures Study

The Demand-Side Grid (dsgrid)
Model Documentation

Elaine Hale,1 Henry Horsey,1 Brandon Johnson,2Matteo Muratori,1 Eric Wilson,1 Brennan Borlaug,1Craig Christensen,1 Amanda Farthing,1 Dylan Hettinger,1Andrew Parker,1 Joseph Robertson,1 Michael Rossol,1Gord Stephen,1 Eric Wood,1 and Baskar Vairamohan212National Renewable Energy Laboratory
Electric Power Research Institute

Suggested Citation

Hale, Elaine, Henry Horsey, Brandon Johnson, Matteo Muratori, Eric Wilson, et al. 2018. The
Demand-side Grid (dsgrid) Model Documentation. Golden, CO: National Renewable Energy
Laboratory. NREL/TP-6A20-71492. https://www.nrel.gov/docs/fy18osti/71492.pdf.

NOTICEThis work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for
Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC3608GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable
Energy Office of Strategic Programs. The views expressed in the article do not necessarily represent theviews of the DOE or the U.S. Government.
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Preface

This report is one in a series of Electrification Futures Study (EFS) publications. The EFS is amulti-year research project to explore widespread electrification in the future energy system of
the United States.
This report documents a new model, the demand-side grid (dsgrid) model, which was developed
for the EFS and in recognition of a general need for a more detailed understanding of electricity
load. dsgrid utilizes a suite of bottom-up engineering models across all major economic
sectors—transportation, residential and commercial buildings, and industry—to develop hourly
electricity consumption profiles for every county in the contiguous United States (CONUS). The
consumption profiles are available by subsector and end use as well as in aggregate. This report
documents a bottom-up modeling assessment of historical (2012) consumption and explains the
key inputs, methodology, assumptions, and limitations of dsgrid.
The EFS is specifically designed to examine electric technology cost advancement and adoption
for end uses across all major economic sectors as well as electricity consumption growth andload profiles, future power system infrastructure development and operations, and the economicand environmental implications of electrification. Because of the expansive scope and the multiyear duration of the study, research findings and supporting data will be published as a series ofreports, with each report released on its own timeframe. Future research to be presented in futureplanned EFS publications will rely on dsgrid to analyze the hourly electricity consumption underscenarios with various levels of electrification. In addition to providing electricity consumption
data for the planned EFS analysis, dsgrid can be used for other analysis outside the EFS researchumbrella.
More information and the supporting data associated with this report, links to other reports inthe EFS study, and information about the broader study are available at www.nrel.gov/efs.

iiiThis report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Acknowledgments

The Electrification Futures Study (EFS) is led by researchers at the National Renewable Energy
Laboratory (NREL) but relies on significant contributions from a large collaboration ofresearchers from the following research organizations: U.S. Department of Energy (DOE),
Evolved Energy Research, Electric Power Research Institute, Lawrence Berkeley National
Laboratory, Northern Arizona University, and Oak Ridge National Laboratory. We would liketo thank all contributors for useful analysis, data, and input throughout the project.
A technical review committee of senior-level experts provided invaluable input to the overall
study, with some committee members sharing thoughtful comments on this specific report as
noted below. Although the committee members offered input throughout the study, the results
and findings from this analysis and the broader EFS do not necessarily reflect their opinions
or the opinions of their institutions. The technical review committee is comprised of thefollowing individuals:
Doug Arent, Joint Institute for Strategic Energy
Analysis (committee chair)

Jonathan Hughes, University of Colorado Boulder

Sam Baldwin, DOE

Michael Kintner-Meyer, Pacific Northwest
National Laboratory

Steve Brick, Clean Air Task ForceJohn Larsen, Rhodium Group

Steve Clemmer, Union of Concerned Scientists

Bryan Mignone, ExxonMobil Research andEngineering Company

Laura Cozzi, International Energy Agency

Granger Morgan, Carnegie Mellon University

Francisco de la Chesnaye, Electric Power
Research Institute

Patrick Riley, GE Global Research

Keith Dennis, National Rural Electric Cooperative
Association

Laurie ten Hope, California Energy Commission

Carla Frisch, DOE

Joan Ogden, University of California Davis

Arnulf Grubler, International Institute for Applied
Systems Analysis

Susan F. Tierney, Analysis Group, Inc.

Howard Gruenspecht, Massachusetts Institute ofTechnology

For this report, the authors are grateful to the following individuals for their thorough reviews,
comments, and suggestions:•John Agan, Greg Dierkers, Paul Donohoo-Vallett, Robert Fares, Rachael Nealer,
Kara Podkaminer, Brian Walker, and Jenah Zweig (DOE)•

Erin Boedecker and William Booth (U.S. Energy Information Administration)•

Lester Shen (Center for Energy and Environment)•

Tom Eckman (Northeast Power Coordinating Council, Inc.)•

Joshua Eichman, Jack Mayernik, Colin McMillan, Daniel Steinberg, Doug Arent, andDan Bilello (NREL)
iv

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We are grateful to Trieu Mai (NREL) for his leadership of the EFS and review of this work.
Primary funding for the EFS is provided by the DOE Office of Energy Efficiency and
Renewable Energy Office of Strategic Programs. We especially thank Paul Donohoo-Vallett andSteve Capanna of DOE for support, leadership, and review of this report and the EFS as a whole.
We also thank Gian Porro and Dani Salyer (NREL) for their program and project management
support, and Mike Meshek (NREL) for editing support. Any errors and omissions are the sole
responsibility of the authors. This research was funded under contract number DE-AC3608GO28308.

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List of Acronyms

ACS
ADOPT
AEO
AMY
ANSI
AWWARF
BCAP
BFG
BIT
BLQ
CBECS
CBP
CEE
CEMS
CHP
CONUS
DFO
DG
dGen
DHI
DHR
DHW
DNI
DNR
DOE
DOE CHP DB
DPV
EFS
EIA
EPA
EPRI
EPW
ERCOT
EST
EVI-ProeVMT
FAA

American Community Survey
Automotive Deployment Options Projection Tool
Annual Energy Outlook (U.S. EIA)
actual meteorological year
American National Standards InstituteAmerican Water Works Association Research Foundation
Building Codes Assistance Project
blast furnace gas
bituminous coal
black liquor
Commercial Buildings Energy Consumption SurveyCounty Business Patterns (U.S. Census Bureau)
Center for Energy and Environment
continuous emission monitoring systems
combined heat and power
contiguous United Statesdistillate fuel oil
distributed generation
Distributed Generation Market Demand (model)
diffuse horizontal irradiancediffuse horizontal radiation
domestic hot water
direct normal irradiancedirect normal radiation
U.S. Department of Energy
U.S. DOE Combined Heat and Power Installation Database
distributed photovoltaics
Electrification Futures Study
U.S. Energy Information Administration
U.S. Environmental Protection Agency
Electric Power Research Institute
EnergyPATHWAYS weather (file type)
Electric Reliability Council of Texas
Eastern Standard Time
Electric Vehicle Infrastructure Projection Toolelectric vehicle miles traveled
Federal Aviation Administration
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FERC
GCAM
GHI
GHR
GTM
GW
HDV
HVAC
IAC
IAM
IEA
IGATE-E
IPCC
ISO
LBNL
LDV
LFG
LIG
MDV
MECS
MG
MISO
MRAE
MSB
NAHB
NAICS
NEEA
NEMS
NESSIE
NGA
NREL
NSRDB
NTD
NYISO
ORNL
OTR
PAT
PBA

Federal Energy Regulatory Commission
Global Change Assessment Modelglobal horizontal irradianceglobal horizontal radiation
Greentech Media
gigawatt
heavy-duty vehicle
heating, ventilation, and air conditioning
industrial assessment center
integrated assessment modelInternational Energy Agency
Industrial Geospatial Tool for Energy Evaluation
Intergovernmental Panel on Climate Change
independent system operator
Lawrence Berkeley National Laboratory
light-duty vehicle
landfill gas
lignite coalmedium-duty vehicle
Manufacturing Energy Consumption Survey
million gallons
Midcontinent Independent System Operator
mean relative absolute error
biogenic municipal solid waste
National Association of Home Builders
North American Industry Classification System
Northwest Energy Efficiency Alliance
National Energy Modeling System
National Electric System Simulation Integrated Evaluator
National Governors Association
National Renewable Energy Laboratory
National Solar Radiation Database
National Transit Database
New York Independent System Operator
Oak Ridge National Laboratory
Other
Parametric Analysis Tool
principle building activity
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PEV
PNNL
PV
RBSA
RBSAM
RECS
RFO
RTO
SAE
SEDS
SERA
SIC
SUB
T&D
TMY
ULRD
UZA
WDS
WECC

plug-in electric vehicle
Pacific Northwest National Laboratory
photovoltaics
Residential Building Stock Assessment
Residential Building Stock Assessment Metering
Residential Energy Consumption Survey
residual fuel oil
regional transmission organization
statistically adjusted end-use
State Energy Data System
Scenario Evaluation, Regionalization and Analysis (model)Standard Industrial Classification
subbituminous coaltransmission and distribution
typical meteorological year
Utility Load Research Dataurbanized area
wood/wood waste solids
Western Electricity Coordinating Council

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Executive Summary

Electrical load is the backdrop for all power systems analysis. As a coequal partner in the supplydemand balance that must be maintained on electrical grids at all times and for all timescales,load is a major source of variability and uncertainty in grid operations. In the context of ourevolving energy systems, with increasing shares of variable renewable energy, potential
electrification of transport and other end uses, and the emerging ability to control energy use at
the kilowatt-scale using grid-level information, obtaining a deeper understanding of electricalload is more important now than it has ever been before. Despite this, many studies of future grid
systems understandably continue to place more emphasis on supply-side resources such as
generation and transmission, given that they are fewer in number and are better understood interms of cost and performance.
Load forecasting has been at the heart of utility planning for decades, but it is typically done in a
top-down manner that lacks the granularity in time, geography, end use, and technology that isneeded to explore the potential impact of technological shifts. Purely econometric methods that
rely on historical load data combined with projections of economic and demographic parameters
are common. More sophisticated methods include combining econometric techniques withsimple engineering models of a limited number of key end uses. Nonetheless, there is evidence
that these methods may be falling short. For example, Carvallo et al. (2017) find systemic
overestimates of load growth in utility integrated resource plans. These overestimates arecertainly partially explained by the recession that followed the 2008 financial crisis but are
persistent enough to suggest other factors (e.g., energy efficiency adoption and performance)
may have been systematically misestimated. This suggests that more complementary, bottom-up
engineering- or physics-based modeling that incorporates technology and behavioral detail may
be a valuable addition to the load forecasting process.
Analyses exploring future scenarios of the power sector also typically employ relatively simple
scaling of historical loads. To some extent, this is a matter of necessity; renewable integration
studies have demonstrated the importance of modeling systems with significant quantities ofwind or solar generation using time-synchronized load, wind, and solar data. Because it is netload that is most important for system operations, these timeseries must all reflect the same
weather as it was experienced simultaneously across the region of interest. Thus, historicalhourly load data are taken from the same year from which the wind and solar data sets arederived, and a basic scaling factor is applied. For example, MacDonald et al. (2016) project
hourly load in 2030 using a single growth rate assumption of 0.7% applied to all hours. Although
expedient, this methodology is insufficient to capture the impact of significant demand-side
technological change, such as widespread electrification, which could drastically impact load
shapes and demand-side flexibility.
The primary purpose of the demand-side grid model (dsgrid) is to fill these gaps by creating
comprehensive load data sets at a sufficient temporal, geographic, sectoral, and end-use
resolution to enable detailed analyses of current patterns and future projections of end-use load.
Furthermore, the dsgrid platform uses a bottom-up methodology that allows highly resolved
analysis of “what-if” scenarios. dsgrid leverages detailed sectoral energy models to provide
hourly time series of load by subsector, end use, and county covering a full year (see Figure ES1). Although dsgrid currently emphasizes electricity load data, its component sector models forix
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residential buildings, commercial buildings, and industry provide information on other fuel use,
including natural gas. The data sets can thus be leveraged to support analysis of numerous
demand-side technology-driven changes, such as energy efficiency, electrification, andoperational flexibility (i.e., demand response). The electricity use data are time-synchronizedwith solar and wind data sets so as to be suitable for use in power systems analysis.

Figure ES-1. dsgrid provides load data for the contiguous United States at high geographic,
temporal, and sectoral resolution.

This report documents dsgrid and its initial data set, which covers electricity demand in thecontiguous United States (CONUS) for the historical year of 2012. We start with a historical year
to enable model calibration and validation, as well as the development of a mathematicaldescription of our model residuals that can be applied to future-year “what-if” scenarios toremove known errors between our bottom-up modeling and historical load shapes. This work ispart of the Electrification Futures Study (EFS), 1 for which the dsgrid team will be developing
future load snapshots that describe projected year 2050 under baseline and a range ofelectrification assumptions.

1

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Within dsgrid’s architecture, each sector’s energy use is modeled with separate methodologies.
The core sector models, which provide the most detailed level of modeling available in dsgrid,cover about 80% of 2012 annual electricity demand (Figure ES-2). 2 An additional 17% is thenrepresented with sector-level gap models, which are coarser in both inputs and outputs than the
fully detailed sector models, but nonetheless leverage some of the core data (e.g., load shapes)
and provide hourly demand timeseries resolved at least to the subsector and state level. The
remaining 3% is electricity used for unmodeled commercial subsectors.

Figure ES-2. dsgrid models about 80% of 2012 U.S. electricity use in detail.

Brief descriptions of the detailed sector methodologies follow:•

Residential and Commercial Buildings: Building loads are estimated using ResStockand ComStock, which use similar statistical methodologies and OpenStudio modelinginfrastructure to simulate the U.S. single-family detached and commercial building stock
electricity consumption by end-use. These models sample from thousands of probability
distributions to produce hundreds of thousands of EnergyPlus simulations, which are thenweighted to represent subsector building stocks at the county level. This detailed
modeling covers single family homes and commercial buildings mappable to the 16 DOE
commercial “prototype buildings” (Goel et al. 2014).

•

Industrial Manufacturing: Industrial manufacturing loads are modeled with theIndustrial Geospatial Analysis Tool for Energy Evaluation (IGATE-E), which uses plantlevel databases, the Manufacturing Energy Consumption Survey, and the Electric Power
Research Institute’s Load Shape Library to construct hourly time series of electricity use
by manufacturing subsector and end use. Because manufacturing processes vary greatly,
IGATE-E does not attempt direct simulation of loads but rather compiles data fromThat is, 80% of the site demand is from residential buildings, commercial buildings, industry, and transport.
This measure excludes such subsectors as public water supply, municipal wastewater treatment, and exterior
roadway lighting.

2

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multiple sources and applies statistical techniques to estimate energy consumption down
to the end-use level. Because IGATE-E only models manufacturing, the additional
industrial sectors of agriculture, mining, and construction comprise our industrial gap
model.
•

Transportation: Given the focus in this report on constructing a model of historicalelectricity use for the year 2012, and the very low deployment of plug-in electric vehicles
in that year, we describe the detailed transportation modeling methods that will be used infuture work to capture EV location and charging; but no detailed sector-level results fortransportation are presented. 3 When this capability is developed for the EFS future modelyear snapshots, on-road plug-in electric vehicle operation will be described using the
Scenario Evaluation and Regional Analysis (SERA) model and the Electric Vehicle
Infrastructure Projection Tool (EVI-Pro). SERA describes vehicle infrastructure
requirements and will be used to disaggregate vehicle adoption to the county level. EVIPro simulates hourly charging profiles based on travel data and charging preference
assumptions (e.g., residential charging as opposed to reliance on public charging). The
historical 2012 data set includes a transportation gap model that describes electricity use
in passenger trains.

While the sector models cover approximately 80% of annual electricity demand, they do notprovide full coverage of all electricity use in the United States, nor do they account for such load
modifiers as distributed generation or transmission and distribution (T&D) losses. The model
therefore incorporates additional data sources and uses them to create gap models, distributed
generation models, and a model of losses (Figure ES-3). The sectoral gap models describe
building types not modeled in detail; electricity used for agriculture, mining and construction;and electricity used for passenger rail transport. dsgrid additionally contains supplemental gap
models that represent electricity used for municipal water services and outdoor lighting.
Distributed generation from solar photovoltaics, combined heat and power, and other distributed
thermal generators is estimated on hourly, state, and sectoral bases using a variety of data
sources. The distributed generation data combined with historical electricity sector data are key
to enabling the calculation of model residuals and related visualizations used to calibrate andvalidate the model. In total, the dsgrid model structure is a confederation of data sets andprogrammatic methods that are available to aggregate, disaggregate, visualize, and performstatistical analysis on an overall description of U.S. electricity demand for one model year.

The data visualized in Figure 2 for Transport consists of a small orange sliver topped with a longer blue sliver; theblue sliver is labeled “Passenger Rail (95%).” The orange sliver represents 381 GWh of electricity used in the U.S.
for light-duty vehicles in 2012 that is not modeled in this version of dsgrid.

3

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Figure ES-3. dsgrid input data architecture
All acronyms are defined in the acronyms list on page vi.

Data Set Summary

The dsgrid historical snapshot for 2012 is a more highly resolved starting point for power sector
studies than has heretofore been available. Except for the transportation gap model, which is at
the state level, the detailed sectoral models and the sectoral gap models are available at thecounty level by subsector and end use. End use breakdowns are provided for the detailed sector
models (residential and commercial buildings, and industrial manufacturing) as well as for the
residential gap model. The detailed building models include hourly data on natural gas anddistrict heating and cooling use, in addition to electricity. For all fuels, building energy use isreported across nine end uses: fans, pumps, space heating, space cooling, heat rejection(commercial only), interior lights, exterior lights, water systems, and interior equipment.
Industrial manufacturing electricity use is reported for the 12 U.S. Energy Information
Administration (EIA) Manufacturing Energy Consumption Survey end uses: conventional boiler
use; process heating; process cooling and refrigeration; machine drive; electrochemical
processes; other process use; facility heating, ventilation, and air conditioning (HVAC); facility
lighting; other facility support; onsite transportation; other nonprocess use; and end use notreported.

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The data set is summarized at the annual level for the contiguous United States in Table ES-1.
The detailed and gap models described above, plus gap models for roadway and parking outdoor
lighting and for public water supply and wastewater treatment (county-level, based on per-capita
energy use estimates) form the dsgrid-core components. Together with the hourly residuals, 4
these components, which are shaded green in Table ES-1, provide an hourly estimate of site
electricity use at the state level. To obtain load profiles that need to be met at the bulk power
level, one can subtract the contribution from distributed generation and then add in the estimate
of T&D losses as appropriate depending on model context. These components are shaded blue.
Table ES-1. Summary of Contiguous U.S. Electricity Use in Terawatt-Hours, Top-Down and
Represented in dsgrid
ComponentType

Component
Name

Top-down

Hourly loada

3,910

DerivedT&D losses

199

Top-down

Annual energyb

dsgridDistributed generation

dsgrid-core

Gap models

218

dsgrid-core

Detailed sector models

1,169

DerivedTotal site energyc

1,372

DerivedAnnual sector residualsd-15

DerivedHourly residualse

Residential

Commercial

Industrial

Transport

Total

1,370

1,350

981

7

3,708

3

31

204

–

237

454

184

6

862

1,107

893

–

3,170

1,381

1,184

7

3,945

-180

107

1

-87
-126

a FERC Form 714 and independent system operator (ISO) reportingb U.S. Energy Information Administration (EIA) Form 861
c Total site energy is the top-down annual energy plus distributed generation. This is all the load we are expecting

to model with the bottom-up detailed sector and gap models.

d The sector level residuals are equal to the total site energy minus the gap and detailed sector model components.
e The hourly residuals reported in the Total column are the sum of the state-level hourly residuals, which factor in top-

down hourly load, T&D losses, distributed generation, and the dsgrid-core model components.

dsgrid model components necessary to represent site-energy use at the hourly level are shaded green.
Components that may be factored in to estimate bulk power system load are shaded blue-grey.

Although the top-down data do not exist to compute hourly residuals resolved by sector, the topdown annual energy data available from U.S. Energy Information Administration (EIA) Form
861, along with our sectoral estimate of distributed generation, do provide the means to computesectoral residuals on an annual basis. Based on this total site energy estimate, we see from Table
ES-1 that for the contiguous United States, about 35%, 35%, 30%, and 0.2% of site electricity
use is attributable to residential, commercial, industrial, and transportation subsectors
respectively; dsgrid bottom-up estimates capture this energy use within ±1% for the residential
sector. For commercial, industrial, and transport, dsgrid estimates fall within ± 15%.
Examining the data at this most aggregated level is helpful, but it belies model discrepancies thatare apparent when the data are examined at a finer level of temporal or geographic resolution.
The hourly residuals are computed by comparing the bottom-up load data to top-down bulk power system hourly
load data and factoring in T&D losses and distributed generation.
4

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For example, Figure ES-4 depicts the dsgrid bottom-up load data (detailed sector models and gap
models) and T&D losses alongside the historical hourly load data, and the historical hourly load
data plus our distributed generation estimates. From this, we can see that our bottom-up
modeling is able to capture seasonal load shape changes (e.g., the double-peak common in
winter, and the single afternoon peak common in summer), but it regularly exaggerates thedifferences between weekday and weekend energy use, as well as between daytime andnighttime energy use, the latter especially during cooling season (e.g., summer and spring).
Such discrepancies between the modeled results and the historical data occur because we do not
artificially constrain our bottom-up models with top-down data. The model residuals that result
therefore point to aspects of our bottom-up understanding of demand-side energy systems thatneed improvement.

Figure ES-4. Bottom-up sector model and gap model load in gigawatts (GW) compared to bulk-level
historical hourly load for the entire contiguous United States (CONUS), for four representative weeks
Each week starts on a Sunday, and all data are plotted time-synchronized in Eastern Standard Time.

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An initial effort to understand the information contained in our hourly and sectoral residuals isshown in Figure ES-5, which depicts energy-weighted average fit statistics at different levels ofgeographic, temporal, and sectoral aggregation. The calculation details are provided in the bodyof the report, but in all cases, what is shown is 1 minus a measure of relative error (i.e., 1.00
indicates zero error) averaged over geographic units using the total annual site energy estimate intotal or by sector as the weight.

Figure ES-5. dsgrid fit statistics for total site energy as a function of geographic, temporal, andsectoral resolution
At the most aggregated level, dsgrid bottom-up sectoral and gap models capture the expected site load withina relative error of 4%. At the least aggregated levels for which we calculate residuals, the energy-weighted mean
relative absolute error is about 20% for hourly electricity, and the energy-weighted relative absolute error ranges from35% (transportation) to less than 10% (residential) for annual energy use by sector.
The levels of geographic resolution are states (48 plus Washington, D.C.), state groups (24 described in the bodyof the report), census divisions (9), census regions (4), and the CONUS (1).

First, examining the hourly electricity residuals, we see confirmation of what we saw in Table
ES-1, namely that at the annual level aggregated to all of CONUS, the dsgrid bottom-up models
captures the site energy represented in the historical hourly load data combined with the dsgrid
distributed generation models to within a relative error of 4%. However, if we examine themodel at the most resolved level for which hourly residuals are available (state-level and hourly),
the level of fit drops almost to 80%. Where is most of the fit lost? Temporally, the first large gap
occurs when we go from weeks to days; this likely speaks to the weekday-weekend discrepancy
apparent in Figure ES-4. The daytime-to-nighttime shifts similarly show up in the difference
between the hour and day curves. Geographically, there is a large difference between census
divisions and census regions, and a more modest but significant difference between states andstate groups. For these hourly residuals, which rely on a disaggregation of system load datareported by independent system operator (ISO) region or FERC Form 714 planning region, axviThis report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

significant portion of the error introduced by disaggregating from the state group to the statelevel may be attributable to that process (and not to the bottom-up modeling alone).
Examining the sectoral residuals, the relative maturity of the residential modeling is immediately
apparent, as its level of agreement with the total annual energy use reported by state on EIA
Form 861 plus our distributed generation estimates is above 92% at all levels of disaggregation(down to states). The level of fit for all other sectors is below 85% at the state level, but it
is greater than 80% for the other two main electricity-consuming sectors: commercial andindustrial.
Overall, these results show that we can model U.S. electricity load at high resolution, especially
if we leverage the model residuals computed here to patch the now-revealed discrepancies
between our modeling and historical data. We also know the likely sources of some of these
discrepancies. For commercial buildings, we are aware of inaccuracies and uncertainties related
to count and size of commercial buildings by type, especially for those building types not well
represented in CoStar. In contrast, industrial manufacturing plant locations are fairly wellknown, 5 but because IGATE-E does not directly model the energy use of industrial processes,
it relies on the DOE industrial assessment centers (IAC) database to create estimates of energy
intensity. Relying solely on the IAC Database is problematic because it underrepresents
industries dominated by very large plants and it may be subject to selection bias. Low data
availability further necessitates using energy intensity metrics based on number of employees
rather than other normalization factors that may be better correlated to energy use such as annual
sales. The temporal discrepancies apparent in the hourly residuals are not all that surprising
given this was the sector modeling teams’ first time providing hourly modeling results as an end
product. Demand-side energy research has historically had a much stronger focus on pure energy
efficiency measured on an annual basis than on the temporal specificity required to understandinteractions with the grid. Moving forward, we expect to conduct more hourly and subhourly
calibration at the sectoral, subsectoral, and end-use levels using utility- and facility-scale metereddata to develop stochastic modeling of operational schedules, control set-points, and occupant
behavior.

Looking Forward

dsgrid is a new model designed to provide a solid basis for exploring questions related to future
electricity load and its relationship to grid operations. Because it is a new model, thisdocumentation represents the beginning of an investigation more so than the end of an analysis.
The model validation documented in this report makes plain the fact that the commercial andindustrial models in particular need more sectoral-level calibration, and that all the models would
benefit from additional temporal calibration. To that end, additional calibration of the building
models to metered data is both ongoing and in various stages of planning as future work. To
make the model usable in the meantime, we are also developing methods to mathematicallysummarize the model residuals in a manner that can be applied to dsgrid scenarios built by
modifying the baseline model documented in this report (e.g., to represent load in future years).

5

IGATE-E relies on the MNI database for this information.

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Subsequent stages of the EFS will include developing dsgrid future-year snapshots of 2050
electrical load based on the PATHWAYS outputs for the EFS electrification scenarios (Mai et al.
2018) and evaluating the potential flexibility of those future loads. Under most scenarios, we
expect significant growth in on-road electric vehicle deployment by 2050. For this reason,transportation, while not a significant part of the dsgrid historical load snapshot documented
here, is anticipatorily included in the methodological documentation.
In addition to the work planned for EFS, the dsgrid data set and modeling framework now exist
as a resource for investigating questions about U.S. electricity load, from regional differencesin coincidence with wind and solar generation, to the potential impact of particular energy
efficiency measures or demand response programs. Some of the underlying data sources are
actually more granular than the county level, so we also anticipate interest in more-localized,
distribution-level modeling. The data on fuel use other than electricity, especially for the
residential and commercial building sectors, could also enable more detailed analysis ofinteractions between natural gas, electricity, and water networks.

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Table of Contents

Executive Summary ................................................................................................................................... ix
1

2

Data Set Summary ............................................................................................................................... xiii
Looking Forward ................................................................................................................................ xvii
The Need for a Detailed Model of Electrical Load ............................................................................. 1

1.1

1.2

Model Architecture: A Composite Picture Built Up from Sector Models...................................... 16

2.1
2.2

2.3

3
4

Overview of Load Models Described in the Literature ................................................................. 4
1.1.1 Energy System and Economy Models.............................................................................. 4
1.1.2 Load Models and Projections for Electricity System Planning ........................................ 6
1.1.3 Models of Electricity Load Flexibility ............................................................................. 8
1.1.4 Sectoral Models ................................................................................................................ 9
Scope of dsgrid ............................................................................................................................ 14

Architectural Overview ............................................................................................................... 16
Sector Models.............................................................................................................................. 20
2.2.1 Residential Sector........................................................................................................... 20
2.2.2 Commercial Sector ......................................................................................................... 29
2.2.3 Industrial Sector ............................................................................................................. 37
2.2.4 Transportation Sector ..................................................................................................... 42
2.2.5 Summary ........................................................................................................................ 47
Gap and Derived Models............................................................................................................. 52
2.3.1 Non-Sectoral Gap Models .............................................................................................. 52
2.3.2 Distributed Generation Models ...................................................................................... 56
2.3.3 Electricity Sector Load Data .......................................................................................... 59
2.3.4 Derived Models .............................................................................................................. 61

Historical Year Model Overview ........................................................................................................ 67
Future Work......................................................................................................................................... 88

4.1
4.2
4.3

Additional ComStock Calibration ............................................................................................... 88
EFS Scenarios ............................................................................................................................. 88
Power System Geography ........................................................................................................... 89

References ................................................................................................................................................. 90
Appendix A.
Residential Sector Model Details .............................................................................. 104

A.1 Data Sources and Methodology Details .................................................................................... 104
A.2 Additional Calibration Details................................................................................................... 105

Appendix B.

Commercial Sector Model Details ............................................................................ 107

Appendix C.

Industrial Sector Model Details ................................................................................. 120

Appendix D.

Transportation Sector Model Details........................................................................ 126

Appendix E.

Model Coverage Analysis Details ............................................................................. 132

B.1
B.2
B.3
B.4
B.5

ComStock Overview ................................................................................................................. 107
Commercial Building Characteristics ....................................................................................... 107
Commercial Building Stock Data Sources ................................................................................ 109
Model Coverage and Gaps ........................................................................................................ 112
Modeling Details ....................................................................................................................... 119

C.1 IGATE-E: Energy Consumption ............................................................................................... 120
C.2 IGATE-E: Load Shapes ............................................................................................................ 123
C.3 Industrial Gap Model ................................................................................................................ 125

D.1 The National Transit Database (NTD) ...................................................................................... 126
D.2 Methods ..................................................................................................................................... 126
D.3 Results ...................................................................................................................................... 128
E.1 Method ...................................................................................................................................... 132
E.2 Results ...................................................................................................................................... 135

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Appendix F.
Historical Year Model Results, CONUS-Level Description of Gap Models andDistributed Generation ..................................................................................................................... 141

F.1
F.2
F.3
F.4

Commercial Gaps ...................................................................................................................... 141
Industrial Gaps .......................................................................................................................... 145
Transportation Gaps .................................................................................................................. 145
Distributed Generation .............................................................................................................. 147

G.1
G.2
G.3
G.4
G.5
G.6
G.7
G.8
G.9

West North Central.................................................................................................................... 149
East North Central ..................................................................................................................... 162
New England ............................................................................................................................. 175
Mid Atlantic .............................................................................................................................. 188
South Atlantic............................................................................................................................ 202
East South Central ..................................................................................................................... 217
West South Central.................................................................................................................... 232
Mountain ................................................................................................................................... 247
Pacific ...................................................................................................................................... 263

Appendix G.

Historical Year Model Results by Census Division ................................................ 148

Appendix H.
Appendix I.
Appendix J.

Weather File Methodology ......................................................................................... 278
Combined Heat and Power and Distributed Thermal Model .................................. 280
Raw State-Level System Loss Factors .................................................................... 286

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List of Figures

Figure ES-1. dsgrid provides load data for the contiguous United States at high geographic, temporal,
and sectoral resolution. ............................................................................................................ x
Figure ES-2. dsgrid models about 80% of 2012 U.S. electricity use in detail. ............................................ xi
Figure ES-3. dsgrid input data architecture ............................................................................................... xiii
Figure ES-4. Bottom-up sector model and gap model load in gigawatts (GW) compared to bulklevel historical hourly load for the entire contiguous United States (CONUS), for four
representative weeks .............................................................................................................. xv
Figure ES-5. dsgrid fit statistics for total site energy as a function of geographic, temporal, andsectoral resolution ................................................................................................................. xvi
Figure 1. Load models summarized by their geographic, temporal, and sectoral extents and resolution... 15
Figure 2. dsgrid input data architecture ...................................................................................................... 17
Figure 3. ResStock simulations use 216 climate subregions, each represented by a single weather
station. .................................................................................................................................... 23
Figure 4. Average of residential load profiles from eight electric utilities available in the Utility
Load Research Data (ULRD) ................................................................................................. 26
Figure 5. The ResStock plug load schedule was updated to use the measured plug load profile fromRBSAM (mid-day valley eliminated). ................................................................................... 26
Figure 6. The ResStock lighting schedule algorithm was updated to eliminate the deep mid-day valley
and more closely match measured lighting use from RBSAM. ............................................. 27
Figure 7. After modification, the ResStock lighting schedule algorithm results in an improved match
to the RBSAM data for all months of the year....................................................................... 27
Figure 8. Number of IAC assessments by industry and year (as of March 2018) ...................................... 38
Figure 9. Transportation modeling scheme for the road subsector ............................................................. 43
Figure 10. EVI-Pro model structure............................................................................................................ 46
Figure 11. dsgrid models about 80% of 2012 U.S. electricity use in detail. ............................................... 48
Figure 12. dsgrid models about 76% of 2012 U.S. site energy use in detail. ............................................. 49
Figure 13. Diurnal profiles for (a) residential water use (AWWARF 1999) and (b) dsgrid municipal
water services gap model ....................................................................................................... 54
Figure 14. Sample single-day (a) solar radiation profile and (b) the outdoor lighting profile that resultsfrom applying the transformation described above ................................................................ 56
Figure 15. CEMS CHP plant capacity factor profiles ................................................................................. 58
Figure 16. Raw state-level loss factors (a) derived from utility reported losses (EIA Reported) and(b) estimated by comparing historical hourly supply and annual metered load data (FERCEIA estimate), ordered by (c) their difference (estimate - reported) ...................................... 62
Figure 17. Loss factors for state groupings ................................................................................................. 63
Figure 18. Model residuals are calculated using electricity sector data and distributed generation models.
................................................................................................................................................ 65
Figure 19. Bottom-up detailed sectoral model and gap model load compared to bulk-level historicalhourly load for all of the CONUS in 2012, for four representative weeks ............................ 69
Figure 20. dsgrid hourly residuals plotted along with the bottom-up model data, T&D loss estimates,
and historical bulk power load data, for the contiguous United States .................................. 70
Figure 21. dsgrid fit statistics for total site energy as a function of geographic, temporal, and sectoral
resolution................................................................................................................................ 72
Figure 22. Historical and dsgrid load duration curves for the CONUS in 2012 ......................................... 73
Figure 23. Residential electricity use diurnal patterns by season, modeled for the CONUS in 2012......... 75
Figure 24. Commercial electricity use diurnal patterns by season, as modeled by ComStockfor the CONUS in 2012 ......................................................................................................... 78
Figure 25. Industrial electricity use diurnal patterns by season, modeled for the CONUS in 2012 ........... 87
Figure 26. Relationship between dsgrid and decadal-scale adoption models ............................................. 88xxi
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Figure A-1. Comparison of modeled and measured daily kWh load for average single-family homes
in the ComEd service territory (2012 data; homes without electric space heating) ............. 105
Figure A-2. Comparison of modeled and measured daily kWh load for average single-family homes
in the ComEd service territory (2012 data; homes with electric space heating) .................. 106
Figure B-1. Workflow used to characterize the U.S. commercial building stock and model energy use
with county-level granularity ............................................................................................... 107
Figure B-2. Visualization of probability table heirarchy, sampled using a Latin hypercube sampling .... 108
Figure B-3. The four U.S. census regions and nine census divisions, used by CBECS 2012 .................. 109
Figure B-4. Relationship between major data sources and commercial model coverage ......................... 112
Figure C-1. IGATE-E Version 2.0 ............................................................................................................ 120
Figure C-2. NAICS: 3111 (Animal Food Manufacturing) regression results ........................................... 121
Figure C-3. Comparison of IGATE-E estimates to 2014 MECS (optimized) .......................................... 123
Figure C-4. IGATE-E load factor estimates by industry .......................................................................... 124
Figure C-5. NAICS: 3111 (Animal Food Manufacturing) normalized load shape ................................... 125
Figure D-6. The Portland, Oregon (red)-Washington (blue) urban area spans two states ........................ 128
Figure D-7. 2012 annual electricity demand for rail transport by state .................................................... 129
Figure D-8. Sample electrical transit load profiles for California, New York, and Florida...................... 129
Figure E-1. Proportion of annual electricity use in each sectoral modeling category .............................. 135
Figure E-2. Annual total site energy use gaps in sectoral models ............................................................ 137
Figure F-1. Municipal water gap model diurnal load shapes .................................................................... 143
Figure F-2. Outdoor lighting gap model diurnal load shapes for the CONUS in 2012 ............................ 144
Figure F-3. Transportation gap model diurnal load shapes for the CONUS in 2012 ............................... 146
Figure F-4. Distributed generation model diurnal generation patterns for CONUS in 2012 .................... 147
Figure G-1. Bottom-up detailed sectoral and gap model load compared to bulk-level historical hourly
load plus DG estimates for the West North Central census division in 2012 ...................... 149
Figure G-2. dsgrid hourly residuals shown in context for the West North Central census division. ........ 150
Figure G-3. Historical and dsgrid load duration curves for the West North Central census division in
2012...................................................................................................................................... 150
Figure G-4. Residential electricity use diurnal patterns by season, modeled for the West North Central
census division 2012 ............................................................................................................ 152
Figure G-5. Commercial electricity use diurnal patterns by season, modeled for the West North Central
census division 2012 ............................................................................................................ 155
Figure G-6. Industrial electricity use diurnal patterns by season, modeled for the West North Central
census division 2012 ............................................................................................................ 160
Figure G-7. Distributed Generation Model Diurnal Generation Patterns, West North Central census
division 2012 ........................................................................................................................ 161
Figure G-8. Bottom-up detailed sectoral and gap model load compared to bulk-level historical hourly
load plus DG estimates for the East North Central census division in 2012........................ 162
Figure G-9. dsgrid hourly residuals shown in context for the East North Central census division. ......... 163
Figure G-10. Historical and dsgrid load duration curves for the East North Central census divisionin 2012 ................................................................................................................................. 163
Figure G-11. Residential electricity use diurnal patterns by season, modeled for the East North Central
census division 2012 ............................................................................................................ 165
Figure G-12. Commercial electricity use diurnal patterns by season, modeled for the East North Central
census division 2012 ............................................................................................................ 168
Figure G-13. Industrial electricity use diurnal patterns by season, modeled for the East North Central
census division 2012 ............................................................................................................ 173
Figure G-14. Distributed Generation Model Diurnal Generation Patterns, East North Central
census division 2012 ............................................................................................................ 174
Figure G-15. Bottom-up detailed sectoral and gap model load compared to bulk-level historical hourly
load plus DG estimates for the New England census division in 2012................................ 175

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Figure G-16. dsgrid hourly residuals shown in context for the New England census division. ............... 176
Figure G-17. Historical and dsgrid load duration curves for the New England census division in
2012...................................................................................................................................... 176
Figure G-18. Residential electricity use diurnal patterns by season, modeled for the New Englandcensus division 2012 ............................................................................................................ 178
Figure G-19. Commercial electricity use diurnal patterns by season, modeled for the New Englandcensus division 2012 ............................................................................................................ 181
Figure G-20. Industrial electricity use diurnal patterns by season, modeled for the New Englandcensus division 2012 ............................................................................................................ 186
Figure G-21. Distributed Generation Model Diurnal Generation Patterns, New England census division
2012...................................................................................................................................... 187
Figure G-22. Bottom-up detailed sectoral and gap model load compared to bulk-level historical hourly
load plus DG estimates for the Mid Atlantic census division in 2012 ................................. 188
Figure G-23. dsgrid hourly residuals shown in context for the Mid Atlantic census division.................. 189
Figure G-24. Historical and dsgrid load duration curves for the Mid Atlantic census division in 2012 .. 189
Figure G-25. Residential electricity use diurnal patterns by season, modeled for the Mid Atlantic
census division 2012 ............................................................................................................ 191
Figure G-26. Commercial electricity use diurnal patterns by season, modeled for the Mid Atlantic
census division 2012 ............................................................................................................ 194
Figure G-27. Industrial electricity use diurnal patterns by season, modeled for the Mid Atlantic
census division 2012 ............................................................................................................ 200
Figure G-28. Distributed Generation Model Diurnal Generation Patterns, Mid Atlantic
census division 2012 ............................................................................................................ 201
Figure G-29. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the South Atlantic census division in 2012 ................... 202
Figure G-30. dsgrid hourly residuals shown in context for the South Atlantic census division. .............. 203
Figure G-31. Historical and dsgrid load duration curves for the South Atlantic census division in
2012...................................................................................................................................... 203
Figure G-32. Residential electricity use diurnal patterns by season, modeled for the South Atlantic
census division 2012 ............................................................................................................ 205
Figure G-33. Commercial electricity use diurnal patterns by season, modeled for the South Atlantic
census division 2012 ............................................................................................................ 208
Figure G-34. Industrial electricity use diurnal patterns by season, modeled for the South Atlantic
census division 2012 ............................................................................................................ 215
Figure G-35. Distributed Generation Model Diurnal Generation Patterns, South Atlantic census
division 2012 ........................................................................................................................ 216
Figure G-36. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the East South Central census division in 2012 ............ 217
Figure G-37. dsgrid hourly residuals shown in context for the East South Central census division. ....... 218
Figure G-38. Historical and dsgrid load duration curves for the East South Central census divisionin 2012 ................................................................................................................................. 218
Figure G-39. Residential electricity use diurnal patterns by season, modeled for the East South
Central census division 2012 ............................................................................................... 220
Figure G-40. Commercial electricity use diurnal patterns by season, modeled for the East South
Central census division 2012 ............................................................................................... 223
Figure G-41. Industrial electricity use diurnal patterns by season, modeled for the East South
Central census division 2012 ............................................................................................... 230
Figure G-42. Distributed Generation Model Diurnal Generation Patterns, East South Central
census division 2012 ............................................................................................................ 231
Figure G-43. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the West South Central census division in 2012 ........... 232

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Figure G-44. dsgrid hourly residuals shown in context for the West South Central census division. ...... 233
Figure G-45. Historical and dsgrid load duration curves for the West South Central census divisionin 2012 ................................................................................................................................. 233
Figure G-46. Residential electricity use diurnal patterns by season, modeled for the West South
Central census division 2012 ............................................................................................... 235
Figure G-47. Commercial electricity use diurnal patterns by season, modeled for the West South
Central census division 2012 ............................................................................................... 238
Figure G-48. Industrial electricity use diurnal patterns by season, modeled for the West South
Central census division 2012 ............................................................................................... 245
Figure G-49. Distributed Generation Model Diurnal Generation Patterns, West South Central
census division 2012 ............................................................................................................ 246
Figure G-50. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the Mountain census division in 2012........................... 247
Figure G-51. dsgrid hourly residuals shown in context for the Mountain census division....................... 248
Figure G-52. Historical and dsgrid load duration curves for the Mountain census division in 2012 ....... 248
Figure G-53. Residential electricity use diurnal patterns by season, modeled for the Mountaincensus division 2012 ............................................................................................................ 250
Figure G-54. Commercial electricity use diurnal patterns by season, modeled for the Mountaincensus division 2012 ............................................................................................................ 253
Figure G-55. Industrial electricity use diurnal patterns by season, modeled for the Mountaincensus division 2012 ............................................................................................................ 261
Figure G-56. Distributed Generation Model Diurnal Generation Patterns, Mountain census division
2012...................................................................................................................................... 262
Figure G-57. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the Pacific census division in 2012 ............................... 263
Figure G-58. dsgrid hourly residuals shown in context for the Pacific census division. .......................... 264
Figure G-59. Historical and dsgrid load duration curves for the Pacific census division in 2012 ............ 264
Figure G-60. Residential electricity use diurnal patterns by season, modeled for the Pacificcensus division 2012 ............................................................................................................ 266
Figure G-61. Commercial electricity use diurnal patterns by season, modeled for the Pacificcensus division 2012 ............................................................................................................ 269
Figure G-62. Industrial electricity use diurnal patterns by season, modeled for the Pacific census
division 2012 ........................................................................................................................ 276
Figure G-63. Distributed Generation Model Diurnal Generation Patterns, Pacific census division
2012...................................................................................................................................... 277
Figure H-1. Example of alignment of integrated AMY NSRDB solar radiation profiles with TMY....... 278

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List of Tables

Table ES-1. Summary of Contiguous U.S. Electricity Use in Terawatt-Hours, Top-Down and
Represented in dsgrid ............................................................................................................ xiv
Table 1. Positive and Negative Attributes of the Three Major Building Energy Modeling Approachesa .. 10
Table 2. Building Characteristics, Dependencies, and Data Sources.......................................................... 21
Table 3. Residential Subsectoral Gaps: Size and Proxy Timeseries Description ....................................... 28
Table 4. Residential Scaling Factors: 2010–2014 ACS Occupied Dwelling Units Divided by RECS
2009 Dwelling Units, by Building Type and Census Division .............................................. 29
Table 5. Building Characteristics, Dependencies, Data Sources, and Number of Variable Values Usedin the ComStock Modeling Process ....................................................................................... 32
Table 7. Manufacturing Plant Characteristics, Modeling Methods, and Data Sources used by
IGATE-E ................................................................................................................................ 39
Table 8. End-Use and Load Shape Category Mapping ............................................................................... 41
Table 9. Industrial Sector Subsectoral Gaps: Size and Proxy Timeseries Description ............................... 42
Table 10. Vehicle Characteristics, Dependencies, and Data Sources Used in the dsgrid Transportation
Modeling Process ................................................................................................................... 44
Table 11. Transportation Sector Subsectoral Gaps: Size and Proxy Timeseries Description..................... 47
Table 12. Geographic, Temporal, and Sectoral Extents, Resolutions, and Biggest Gaps for dsgrid’s
Constituent Models ................................................................................................................ 51
Table 13. Public Water Supply Energy Intensity and Population Served................................................... 53
Table 14. Municipal Wastewater Treatment Energy Intensity and Quantity Treated ................................ 53
Table 15. Estimates of Absolute and Per-Capita Energy Used for Outdoor Lighting ................................ 55
Table 16. Combined Heat and Power and Distributed Thermal Model: Capacity and Behind-the-Meter
Generation Summary ............................................................................................................. 57
Table 17. Distributed Photovoltaics Capacity and Generation Summary ................................................... 59
Table 18. Top-Down Data on U.S. Power System Demand Used in dsgrid to Define System Losses
and Model Residuals .............................................................................................................. 59
Table 19. Largest Providers of Energy Furnished or Consumed without Charge in 2012 as Reported
on EIA Form 861 ................................................................................................................... 60
Table 20. Loss Factors Applied at the State Level to Create the System Losses Derived Model .............. 64
Table 21. Summary of Contiguous U.S. Electricity Use in Terawatt-Hours, Top-Down and
Represented in dsgrid ............................................................................................................. 67
Table 22. Residential Subsectors, Summary of Electricity by End Use for the CONUS in 2012 .............. 74
Table 23. Residential Electricity Proportions by Subsector and End Use for the CONUS in 2012 ........... 74
Table 24. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the CONUS in 2012 .................................................................................................. 76
Table 25. Commercial Electricity Proportions of Detailed Models by Subsector and End Use
for the CONUS in 2012 ......................................................................................................... 77
Table 26. Industrial Manufacturing Subsectors, Summary of IGATE-E Electricity Results by End
Use for the CONUS in 2012 .................................................................................................. 79
Table 27. Industrial Manufacturing Electricity Proportions for Subsectors and End Uses as Modeled
for the CONUS in 2012 by IGATE-E .................................................................................... 83
Table A-1. List of Data Sources Used to Derive Input Probability Distributions for ResStock ............... 104
Table B-1. Mapping of CoStar Building Combo Codes and DOE Prototype Buildings .......................... 113
Table B-2. Mapping of CBECS Detailed Principal Building Activity (PBAPLUS) and DOE
Prototype Buildings, and Associated Building Counts ........................................................ 116
Table B-3. Mapping of CBECS Detailed Principal Building Activity (PBAPLUS) and CoStar
Combo Codes for those PBAPLUS Categories without a DOE Prototype Match............... 118
Table C-1. Comparison of IGATE-E estimates to 2014 MECS (Initial) .................................................. 122
Table C-2. End-Use and Load Shape Category Mapping ......................................................................... 124

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Table D-1. Sample Utilization Matrix ...................................................................................................... 127
Table D-1. Sample of Energy Estimation Matrix ..................................................................................... 130
Table E-1. Subsectors Categorized as Modeled in Detail, Sectoral Gap Model, or Unmodeled .............. 133
Table E-2. Largest Subsectors Represented by Coarse Gap Models, by Total Electricity Use ................ 136
Table E-3. Largest Unmodeled Subsectors, by Total Electricity Use ....................................................... 136
Table E-4. Largest Subsectors Represented by Coarse Gap Models, by Total Site Energy Use .............. 138
Table E-5. Largest Unmodeled Subsectors, by Total Site Energy Use..................................................... 138
Table E-6. Electrifiable and Non-Electrifiable End Uses by Sector ......................................................... 139
Table E-7. End-Use Energy Share by Model Detail and Electrifiability .................................................. 140
Table F-1. Commercial Gap Model Annual Summary for the CONUS in 2012, by Subsector
and Census Division............................................................................................................. 141
Table F-2. Commercial Gap Model Annual Summary for the CONUS in 2012, Proportions by
Subsector and Census Division ............................................................................................ 142
Table F-3. Municipal Water Gap Model Annual Summary for the CONUS in 2012, Electricity Use
and Proportions by Subsector and Census Division............................................................. 143
Table F-4. Outdoor Lighting Gap Model Annual Summary for the CONUS in 2012, Electricity Use
and Proportions by Subsector and Census Division............................................................. 144
Table F-5. Industrial Gap Model Annual Summary for the CONUS in 2012, Electricity Use andProportions by Subsector and Census Division ................................................................... 145
Table F-6. Transportation Gap Model for Passenger Rail Electricity, Annual Summary for the
CONUS in 2012 by Census Division ................................................................................... 146
Table F-7. Distributed Generation Model, Annual Summary for the CONUS in 2012 ........................... 147
Table G-1. States Listed by Census Region and Division ........................................................................ 148
Table G-2. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, West North Central Census Division ....................................................................... 149
Table G-3. Residential Subsectors, Summary of Electricity by End Use for the West North Central
Census Division in 2012 ...................................................................................................... 151
Table G-4. Residential Electricity Proportions by Subsector and End Use for the West North Central
Census Division in 2012 ...................................................................................................... 151
Table G-5. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the West North Central Census Division in 2012 ................................................... 153
Table G-6. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the West North Central Census Division in 2012 ................................................................ 154
Table G-7. Industrial Manufacturing Subsectors, Summary of Model Results for the West North
Central Census Division in 2012.......................................................................................... 156
Table G-8. Distributed Generation Model, Annual Summary the West North Central Census
Division in 2012 by Sector and DG Type ............................................................................ 161
Table G-9. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, East North Central Census Division ........................................................................ 162
Table G-10. Residential Subsectors, Summary of Electricity by End Use for the East North Central
Census Division in 2012 ...................................................................................................... 164
Table G-11. Residential Electricity Proportions by Subsector and End Use for the East North Central
Census Division in 2012 ...................................................................................................... 164
Table G-12. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the East North Central Census Division in 2012 ..................................................... 166
Table G-13. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the East North Central Census Division in 2012.................................................................. 167
Table G-14. Industrial Manufacturing Subsectors, Summary of Model Results for the East North
Central Census Division in 2012.......................................................................................... 169
Table G-15. Distributed Generation Model, Annual Summary the East North Central Census
Division in 2012 by Sector and DG Type ............................................................................ 174

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Table G-16. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented
in dsgrid, New England Census Division ............................................................................ 175
Table G-17. Residential Subsectors, Summary of Electricity by End Use for the New England
Census Division in 2012 ...................................................................................................... 177
Table G-18. Residential Electricity Proportions by Subsector and End Use for the New England
Census Division in 2012 ...................................................................................................... 177
Table G-19. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by
End Use for the New England Census Division in 2012 ..................................................... 179
Table G-20. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the New England Census Division in 2012.......................................................................... 180
Table G-21. Industrial Manufacturing Subsectors, Summary of Model Results for the New England
Census Division in 2012 ...................................................................................................... 182
Table G-22. Distributed Generation Model, Annual Summary the New England Census Divisionin 2012 by Sector and DG Type........................................................................................... 187
Table G-23. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented
in dsgrid, Mid Atlantic Census Division .............................................................................. 188
Table G-24. Residential Subsectors, Summary of Electricity by End Use for the Mid Atlantic
Census Division in 2012 ...................................................................................................... 190
Table G-25. Residential Electricity Proportions by Subsector and End Use for the Mid Atlantic
Census Division in 2012 ...................................................................................................... 190
Table G-26. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by
End Use for the Mid Atlantic Census Division in 2012....................................................... 192
Table G-27. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the Mid Atlantic Census Division in 2012 ........................................................................... 193
Table G-28. Industrial Manufacturing Subsectors, Summary of Model Results for the Mid Atlantic
Census Division in 2012 ...................................................................................................... 195
Table G-29. Distributed Generation Model, Annual Summary the Mid Atlantic Census Divisionin 2012 by Sector and DG Type........................................................................................... 201
Table G-30. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, South Atlantic Census Division ............................................................................... 202
Table G-31. Residential Subsectors, Summary of Electricity by End Use for the South Atlantic
Census Division in 2012 ...................................................................................................... 204
Table G-32. Residential Electricity Proportions by Subsector and End Use for the South Atlantic
Census Division in 2012 ...................................................................................................... 204
Table G-33. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the South Atlantic Census Division in 2012 ........................................................... 206
Table G-34. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the South Atlantic Census Division in 2012 ........................................................................ 207
Table G-35. Industrial Manufacturing Subsectors, Summary of Model Results for the South Atlantic
Census Division in 2012 ...................................................................................................... 209
Table G-36. Distributed Generation Model, Annual Summary the South Atlantic Census Divisionin 2012 by Sector and DG Type........................................................................................... 216
Table G-37. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, East South Central Census Division ........................................................................ 217
Table G-38. Residential Subsectors, Summary of Electricity by End Use for the East South Central
Census Division in 2012 ...................................................................................................... 219
Table G-39. Residential Electricity Proportions by Subsector and End Use for the East South Central
Census Division in 2012 ...................................................................................................... 219
Table G-40. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the East South Central Census Division in 2012 ..................................................... 221

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Table G-41. Commercial Electricity Proportions of Detailed Models by Subsector and End Use
for the East South Central Census Division in 2012 ............................................................ 222
Table G-42. Industrial Manufacturing Subsectors, Summary of Model Results for the East South
Central Census Division in 2012.......................................................................................... 224
Table G-43. Distributed Generation Model, Annual Summary the East South Central Census
Division in 2012 by Sector and DG Type ............................................................................ 231
Table G-44. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, West South Central Census Division ....................................................................... 232
Table G-45. Residential Subsectors, Summary of Electricity by End Use for the West South Central
Census Division in 2012 ...................................................................................................... 234
Table G-46. Residential Electricity Proportions by Subsector and End Use for the West South Central
Census Division in 2012 ...................................................................................................... 234
Table G-47. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the West South Central Census Division in 2012 ................................................... 236
Table G-48. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the West South Central Census Division in 2012 ................................................................ 237
Table G-49. Industrial Manufacturing Subsectors, Summary of Model Results for the West South
Central Census Division in 2012.......................................................................................... 239
Table G-50. Distributed Generation Model, Annual Summary the West South Central Census
Division in 2012 by Sector and DG Type ............................................................................ 246
Table G-51. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, Mountain Census Division ....................................................................................... 247
Table G-52. Residential Subsectors, Summary of Electricity by End Use for the Mountain
Census Division in 2012 ...................................................................................................... 249
Table G-53. Residential Electricity Proportions by Subsector and End Use for the Mountain
Census Division in 2012 ...................................................................................................... 249
Table G-54. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the Mountain Census Division in 2012 ................................................................... 251
Table G-55. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the Mountain Census Division in 2012 ................................................................................ 252
Table G-56. Industrial Manufacturing Subsectors, Summary of Model Results for the Mountain
Census Division in 2012 ...................................................................................................... 254
Table G-57. Distributed Generation Model, Annual Summary the Mountain Census Division in2012 by Sector and DG Type ............................................................................................... 262
Table G-58. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, Pacific Census Division ........................................................................................... 263
Table G-59. Residential Subsectors, Summary of Electricity by End Use for the Pacific Census
Division in 2012 ................................................................................................................... 265
Table G-60. Residential Electricity Proportions by Subsector and End Use for the Pacific census
division in 2012.................................................................................................................... 265
Table G-61. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by
End Use for the Pacific Census Division in 2012 ................................................................ 267
Table G-62. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the Pacific Census Division in 2012 .................................................................................... 268
Table G-63. Industrial Manufacturing Subsectors, Summary of Model Results for the Pacific Census
Division in 2012 ................................................................................................................... 270
Table G-64. Distributed Generation Model, Annual Summary the Pacific Census Division in 2012by Sector and DG Type ........................................................................................................ 277
Table H-1. Mapping to EnergyPlus Weather File Fields from NSRDB Fields ........................................ 279
Table I-1. Commercial and Industrial Power Plant Capacity and Generation by Prime Mover, per
EIA Forms 860 and 923 ....................................................................................................... 281

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Table I-2. CHP and Thermal DG Capacity Described in EIA Forms 860 and 923 (EIA) and in
the DOE Combined Heat and Power Installation Database (CHP DB)a .............................. 281
Table I-3. Mapping Between EIA and CHP DB Prime Mover and Fuel Type Codesa ............................ 282
Table I-4. Results of Matching CHP Plants in the DOE CHP DB with CHP Plant Reports in 2012
EIA Form 860 and Form 923 Data ...................................................................................... 284
Table J-1. Raw State-Level Loss Factors (a) Based on EIA Form 861 Data Alone (Reported),(b) Based on Comparing FERC Form 714 and EIA Form 861 Data (Estimated),and (c) Their difference (Estimated – Reported) ................................................................. 286

xxix
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1 The Need for a Detailed Model of Electrical Load

Electrical load is the backdrop for all power systems analysis. As a coequal partner in the supplydemand balance that must be maintained on electrical grids at all times and for all timescales,load is a major source of variability and uncertainty in grid operations. Load forecasting has beenat the heart of utility planning for decades, but it is typically done in a top-down manner thatlacks the granularity in time, geography, end use, and technology that is needed to explore thepotential impact of technological shifts. Purely econometric methods that rely on historical load
data combined with projections of economic and demographic parameters are common. Moresophisticated methods include combining econometric techniques with simple engineeringmodels of a limited number of key end uses. Nonetheless, there is evidence that these methods
may be falling short. For example, Carvallo et al. (2017) find systemic overestimates of load
growth in utility integrated resource plans. These overestimates are certainly partially explainedby the recession that followed the 2008 financial crisis but are persistent enough to suggest otherfactors (e.g., energy efficiency adoption and performance) may have been systematicallymisestimated. Forecasting future load is only going to become more difficult and more importantas new demand-side appliances, control technologies, and other distributed energy resources are
adopted, and as wind and solar generation compose a larger portion of our power supply. Thissuggests that more complementary, bottom-up engineering- or physics-based modeling thatincorporates technology and behavioral detail may be a valuable addition to the load forecastingprocess.
In the past, U.S. electricity consumption experienced rapid unexpected change coincident with
important electricity sector developments. For instance, the first wave of air conditioning
adoption in the 1960s and 1970s coincided with the construction of many new petroleum-fueled
generators and was swiftly followed by an oil embargo that made the operation of those newgenerators significantly more expensive than expected (U.S. EIA 2016; Bhatnagar and Rahman
1986; U.S. EIA 2012). Overall, conditions such as these resulted in the traditional growth-based
treatment of electrical load being insufficient. We thus turned to new approaches, including
energy efficiency and demand response (Hurley, Peterson, and Whited 2013; Alliance to Save
Energy 2013).
The pressures on the power system are arguably greater today. Like air conditioning before it,
electric vehicles are poised to become a new large load for many households and businesses(McNerney et al. 2016; Banister et al. 2012; Egbue and Long 2012). Heat pumps are also gaining
market share, primarily in the southern United States, but with a growing potential to compete
in cold climates as well (Baxter and Groll 2017; Lapsa and Khowailed 2014). At the same time,
new generation resources such as wind and solar, and complementary resources such as battery
and other forms of energy storage, are coming online and complicating grid planning andoperations. In the meantime, aggressive energy efficiency efforts in the form of utility programs,
state-level goals, and federal appliance standards have proceeded apace. The International
Energy Agency (IEA) estimates that U.S. energy efficiency investments since 1990 have reduced
the total final energy consumption required to provide the same services by up to 7.44

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quadrillion British thermal units (Quads) per year [2,181 terawatt-hours (TWh) per year] 6 (IEA
2015). Utility planners have sometimes underestimated the future impacts of such energy
efficiency efforts and other causes of low load growth, which has made it more difficult to create
cost-effective plans for satisfying future load while also integrating wind and solar (Kavalec etal. 2016; Carvallo et al. 2017).
Given the impacts of continued adoption of air conditioning and distributed solar photovoltaics,
the nascent adoption of electric vehicles, and the shift of some economic activity away fromindustry and toward more commercial service work, there is some reason to expect that futureload profiles may become peakier (NGA 2016). On the other hand, energy efficiency, energy
storage, and demand response (load scheduling and shifting) technologies, as well as the
emergence of some forms of computing and other miscellaneous electrical loads as a base load
may mitigate this trend such that peak load becomes less of a concern (Schwartz et al. 2017;
Zarakas et al. 2017). Recent data give credence to perhaps both these things happening, withvery different balances being achieved at a regional level. That is, peak and annual load growth
have decoupled in many places but not necessarily in the same direction. For instance, thenortheastern United States has seen peak load growth higher than annual load growth (NGA
2016), while California has been revising its forecasts in the opposite direction (Kavalec et al.
2016). In the South, peak and annual growth rates have been similar in recent years (EIA 2014a).
Because transmission and distribution (T&D) infrastructure are sized to handle peak load (i.e.,
the largest coincident power draw anticipated by system planners or experienced in a givenyear), these distinctions are important to capture with regard to both planning and operations.
Though accurately capturing the split between overall and peak load growth remains important,increasing levels of wind and solar deployment are also moving us beyond this paradigm whereannual demand, peak demand, and coincidence factor 7 are sufficient metrics for describing thestate of current and future electricity loads. Recent renewable integration studies clarify that
future power system operations will focus on balancing net load, that is, load minus all variablegeneration contributions from wind and solar. For systems with large contributions from solar
photovoltaics (PV), it emerges that the daily minimum net-load point is just as important as thedaily peak (net-)load point, and that many days will have a peak in the morning in addition to
one in the afternoon or evening. 8 For systems with large contributions from wind, the mostchallenging times for system operations are even harder to locate, given the seasonal, weekly,
and diurnal variability of the wind resource.

This statistic includes all energy use, not just use in the electricity sector. For 2014, the IEA estimates the U.S.
“total final consumption” equaled 17,458 TWh and that there would have been an additional 2,181 TWh in theabsence of all energy efficiency investments since 1990. This compares to EIA’s reporting of 2014 primary energy
use and electricity sales to the residential, commercial, industry and transportation sectors of 22,138 TWh in total,
with just 3,764 TWh attributed to electricity sales (which like the total final consumption metric, excludes systemlosses) (U.S. EIA 2017).
7
Coincidence factor is calculated over some set of system sub-components, for instance, substations, feeders, or
customers, and is the system peak load divided by the sum of the individual component peaks. The coincidence
factor is 1.0 if all sub-components peak at the same time and otherwise is less than 1.0.
8
Though this is a fairly common pattern in the winter, in areas with high PV penetrations we also see this pattern
emerge in the summer, when traditionally there has been a single clear peak sometime in the afternoon that is
approximately coincident with peak air conditioning loads.
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Renewable integration studies have also demonstrated the importance of modeling systems withsignificant quantities of wind or solar generation using time-synchronized load, wind, and solar
data (Holttinen et al. 2017). Because it is net-load that is most important for system operations,these timeseries must all reflect the same weather as it was experienced simultaneously acrossthe region of interest. For this reason, renewable integration studies have generally relied on
historical hourly load data taken from the same year from which the wind and solar data sets arederived. For example, the following description from MacDonald et al. (2016, Supplementary
Information Section 1.3) is typical:
The electric load data was expanded from 2006–2008 to 2030 estimated levels.
To estimate the 2030 load, quarterly gross domestic product figures from the
Bureau of Economic Analysis (72) were applied to the expansion (andcontraction) of the load to 2011, and then a 0.7% per year growth rate (73) wasapplied to 2030. The result is a 14% increase in the demand for each hour.
However, we know that this methodology is insufficient to capture the impact of significant
demand-side technological change.
The primary purpose of dsgrid is to create comprehensive electricity load data sets at a sufficient
temporal, geographic, sectoral, and end-use resolution to enable detailed analyses of current andfuture projections of end-use load. The data sets can be leveraged to support analysis of demandside energy efficiency, electrification, and operational flexibility (i.e., demand response); andare time-synchronized with solar and wind data sets so as to be suitable for renewable
integration studies.
The initial effort described here is part of the Electrification Futures Study (EFS); 9 thus, ourinitial focus is on developing time-synchronous electricity data suitable for studying the impactof electrified loads on future power systems. This work is proceeding in two stages. What is
documented here is a mesoscale model of current electrical loads built using a variety ofnational-level data sets, bottom-up building energy simulations driven by historical-year weather
files, and a detailed accounting of electricity use in industrial manufacturing and transportation.
We focus initially on modeling a historical year of electricity use to enable model validation,
calibration, and development of an error model that can be carried forward into our future-year
data sets.
In the next stage of the EFS, using dsgrid, we will develop future-year snapshots of electricalload and evaluate the potential flexibility of those future loads. These snapshots will be based on
the EFS electrification scenarios for 2050 as represented in the outputs of theEnergyPATHWAYS model (Williams et al. 2014). Under most scenarios, we expect significant
growth in electric vehicle deployment by 2050. For this reason, transportation, while not asignificant part of the dsgrid historical load snapshot documented here, is anticipatorily includedin the methodological documentation.

9

For more information, see https://www.nrel.gov/analysis/electrification-futures.html.

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1.1 Overview of Load Models Described in the Literature

Previous investigations of these kinds of long-term questions about electricity demand, 10
including how electricity demand impacts power system planning and operations in relation tovarious societal factors and goals, 11 have used models intended to capture an entire energy
system or economy, of which energy demand is a part; load forecasting models used forelectricity system planning 12; models of electricity load flexibility; and sector-specific energy
models. In this section, the authors describe some of the more prominent efforts in each of thesecategories. In Section 1.2, we summarize them in relation to the aims of dsgrid.
1.1.1 Energy System and Economy Models
The National Energy Modeling System (NEMS)
NEMS 13 is a modular energy-economy modeling system used to construct the U.S. Energy
Information Administration (EIA) Annual Energy Outlook (AEO) and to conduct other U.S.
energy systems analysis. Four NEMS modules are of primary interest here: Commercial
Demand, Industrial Demand, Residential Demand, and Transportation Sector Demand. Together,
they comprise all the NEMS end-use demand modules. The demand modules all accept as inputs
energy prices and macroeconomic indicators, and they then compute changes in sector demand
based on assumptions about technology cost, equipment standards, tax and other relevant
policies. Energy consumption by fuel type is output at the U.S. census division level. NEMS theniterates to converge on equilibrium energy prices and quantities (EIA 2009).
The Residential Demand Module and the Commercial Demand Module use the most recent
Residential Energy Consumption Survey (RECS) or Commercial Building Energy Consumption
Survey (CBECS) respectively as their base year and then project residential and commercial
energy use forward several decades. The current end year is 2050. NEMS refers to these as
structural models, that is, they provide an accounting of energy use by tracking building andappliance stock, including both quantity and performance characteristics, which are influenced
by macroeconomic parameters, energy prices, and technology learning effects. The modules
output energy use by fuel type and end use. For commercial buildings, there is a distinction
between major and minor fuels and end uses. The minor fuels and end uses are modeled in lessdetail. Energy efficiency standards, building energy standards, and customer adoption of energy
efficiency and distributed generation technologies (e.g., PV and combined heat and power or
CHP) are some of the main factors captured beyond basic macroeconomic trends (EIA 2017d,
2017e). Residential demand is modeled by tracking housing and appliance stocks (EIA 2017d).
Commercial building energy use is primarily tracked by floor area per building type, overall
energy use intensity (in British thermal units per square feet [kBtu/ft2]), and changes to energy

Greening, Boyd, and Roop (2007); FERC (2009); Boßmann and Staffell (2015); Alstone et al. (2016b); Shoreh etal. (2016); Klingler, Elsland, and Boßmann (2017); Wilson et al. (2017)11
EIA (2017c); Williams et al. (2014); Mai et al. (2014); Melaina et al. (2016); California Energy Commission Staff(2017); Alstone et al. (2017); DOE (2017b)
12Here, we leave aside purely statistical or econometric models (e.g.., regressions on historical load, population andeconomic indicator data coupled with population and economic forecasts), as they are less able to account for the
major technological changes we expect to see over the long term.
13
NEMS: National Energy Modeling System, https://www.eia.gov/outlooks/aeo/info_nems_archive.php
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use intensity, based on equipment efficiency and building shell upgrades. Distributed generation
adoption then reduces the net load seen by the wider energy system (EIA 2017e).
The Industrial Demand Module receives employment data and the value of industrial shipments,
beyond energy price, as additional inputs from the NEMS Integrating Module. These data areused to adjust the demand from 15 manufacturing and 6 non-manufacturing subsectors. Fiveof the manufacturing industries are modeled with detailed process flows (i.e., engineering
representations of individual process steps); two are modeled with detailed end-use accounting;the energy demand of the remaining eight is based on a simpler end-use accounting and a motor
stock model for machine drive electricity use. Manufacturing industries classified as energyintensive are more likely to have a process flow model. All industry, manufacturing and nonmanufacturing is modeled using three end-use components: boilers/steam/cogeneration,buildings, and process/assembly. We have so far been characterizing the process/assembly
portion, which accounts for about 55% of industrial energy use. Building energy use is a small(about 4%) component of industrial energy use, and so is not modeled in detail, but it is a simple
function of employment and industrial output per subsector. Process and building heating is
supplied from the boilers/steam/cogeneration module, which consumes fuels to produce steam
and electricity. Existing CHP plants are represented directly, and new ones are built to meet new
thermal energy requirements, and to consume biomass waste products (e.g., in the pulp and paperindustry). The non-manufacturing industrial sectors are modeled with unit energy consumption
parameters per ton of throughput or dollar of shipments, as well as interactions with theCommercial and Transportation Sector Demand modules. Unit energy consumption is derived
from a variety of data sources specific to agriculture, mining, and construction. The commercial
and transportation demand modules supply parameters concerning construction demand,commercial building energy intensity, and vehicle fuel efficiencies. Benchmarking for the startyear is done with a combination of EIA Manufacturing Energy Consumption Survey (MECS)
and State Energy Data System (SEDS) data (EIA 2014b).
The Transportation Sector Demand module estimates passenger travel demand across severaltransportation modes, and it then uses those estimates along with stock models representing
several vehicle types and their efficiencies by vintage to output energy demand by fuel type andmode. Transportation modes are quite various; they include private and fleet light-duty vehicles;
aircraft; marine, rail, and truck freight; mass transit; military transportation; and recreationalboating. Light-duty cars and trucks are captured in a stock model, and vehicle miles are
estimated at the household and fleet levels. Fifteen alternate fuel vehicle types are modeled
relative to the baseline of conventional gasoline light-duty vehicles. Aircraft energy use is
primarily modeled based on global macroeconomic indicators and fleet efficiency. The freighttransportation modes are modeled together, and they are responsive to the growth in industrial
output. All other transportation modes are lumped into miscellaneous energy demand
submodule (EIA 2016c).
Energy-Economic and Integrated Assessment Models (IAMs)
This class of models represents all the physical, economic, and technological systems related to
the global energy system and its greenhouse gas emissions. The level of modeling detail varies
by model, but at their core, these are simulations models used to design and compare “what-if”
scenarios regarding human energy systems and their interactions with physical and climate
systems (e.g., water and land use, and climate change impacts). These models have been used
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extensively to inform national energy strategies and assess long-term options in the context ofinternational efforts to mitigate climate change impacts (e.g., scenarios used by Edenhofer et al.
[2015]). Given its central role in transformation pathways for global energy systems, the electric
power sector is a major focus of many IAMs. IAMs cover electricity generation to various levelsof detail (Kriegler et al. 2014), projecting long-term energy demand (in TWh consumed) andcapacity requirements (in gigawatts [GW] deployed) based on socioeconomic projections,
technology evolution (e.g., cost and efficiency of supply and demand technologies), and supplydemand balancing factors (e.g., power plants capacity factors). One IAM many U.S. researchers
are familiar with is the Global Change Assessment Model (GCAM). 14
EnergyPATHWAYS
EnergyPATHWAYS is a stock rollover, multi-decadal energy accounting tool designed foranalysis of energy system transformation. 15 It is not a forecasting tool, but it instead simulates
the whole energy economy at high fidelity given user specified decisions and produces energy
demand, service demand, system costs, emissions, and the resulting infrastructure build. The tool
is structured to be reusable for different geographic extents and resolutions, and it has been usedoutside the United States in Europe and Mexico.
Our primary interest for dsgrid is the U.S. model, early versions of which were developed byWilliams et al. (2014). In the U.S. model, most demand-side data come from NEMS while
supply side data comes from a variety of sources, including the National Renewable Energy
Laboratory (NREL). For the EFS, more detailed input data than what is available from NEMS
are being used in some cases, which has required extending some model structures. For instance,electrifiable industrial end uses are being modeled as stocks instead of as exogenously specified
energy requirements, state-level population projections from the University of Virginia
Demographics Research Group are being used instead of census population projections, andcounty-level heating and cooling electricity shapes from NREL for the 2010–2012 weather years
replace a single month-hour average heating and cooling shape for the entire United States.
EnergyPATHWAYS models the electricity system with an annual-hourly dispatch, and it
includes storage, flexible load, and electric fuel production, which informs costs, emissions, andthe need for new infrastructure. In addition, the 8,760 hourly load values are assembled bottomup from energy demand projections and subsectoral or technology-specific normalized electricity
profiles for different geographies. In this way, it captures the changing load-shape patterns fromelectrification, energy efficiency, or growth in service demand.
1.1.2 Load Models and Projections for Electricity System Planning
Renewable Electricity Futures
The Renewable Electricity Futures study (NREL 2012; Hostick et al. 2014) examined a range ofpossibilities for the United States power system in 2050. To understand the impact of electricity
load, the project team bracketed future load with low- and high-demand cases and adjusted
sectoral load shapes to account for changes in some building end uses. The High-Demand
Baseline was based on the 2009 AEO, which required extrapolations for 2030–2050. This was“Global Change Assessment Model,” Joint Global Change Research Institute,
http://www.globalchange.umd.edu/gcam/
15“EnergyPATHWAYS,” Evolved Energy Research, http://energypathways.readthedocs.io/en/latest/
14

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done by continuing electricity intensity trends by households for the residential sector, by unit
floor space for the commercial sector, and per real dollar of shipments for industry. In this case,only industrial sector electricity intensity was projected to decline—by 35% from 2010 to 2050.
The AEO also provided regional load shape information.
The Low-Demand Baseline modeled more significant changes—primarily energy efficiency
gains in all sectors—and significant vehicle electrification. To estimate load shapes for buildings,
overall efficiency projections (in energy use intensity units; e.g., kilowatt-hour [kWh]/ft2-yr)
were applied to a stock model accounting of new, existing, and retrofitted buildings; the resulting
overall energy use was then allocated to end uses based on regionalized projections of end-use
shares per year that accounted for such expectations as there being less efficiency gains for
miscellaneous electrical loads than for other end uses. These steps were executed in the
Lawrence Berkeley National Laboratory Building End-Use Loads Forecaster spreadsheet model,
whose inputs and outputs are NEMS-formatted end-use load shapes, which are hourly and forthree representative days. Industrial load shapes were primarily adjusted by choosing load factorsthat simultaneously fit Electric Power Research Institute (EPRI) load factor data, overall load
duration curve data, and electricity sales by sector. Electric vehicle load curves were adopted
from previous work (Parks, Denholm, and Markel 2007; Markel et al. 2009).
Itron’s Statistically Adjusted End-Use (SAE) Model
The SAE load forecasting approach combines appliance stock models for individual end useswith an econometric model that maps end-use energy estimates to overall expectations for utility
loads (Enterline and Fox 2010). This model is used to construct long-term estimates of energy
efficiency across 30 end uses, and it has been used in several long-term electricity planningstudies (WECC 2013; Enterline and Fox 2010; Carvallo et al. 2017). Temporally, it provides
seasonal and peak adjustments. Sectorally, it provides technical potential energy efficiency
estimates for existing technologies. Residential and commercial building end uses are modeledwith an equipment stock approach and square footage trends. Data sources include NEMS
output, census data on heating shares by state, the Federal Energy Regulatory Commission(FERC) demand response potential study (FERC 2009), and additional data on appliancesaturation and efficiency compiled from state and utility-level studies (Wagner and Barbose
2012).
Power System Planners
The California Energy Commission has been regularly compiling load forecasts covering one
decade since the mid-1990s. The projections are a combination of econometric regressions andsome bottom-up analysis by sector. Kavalec et al. (2016) give low-, mid-, and high-demand
forecasts for annual electricity use and non-coincident peak demand over 20 forecast zones, withdetailed analysis of energy efficiency, distributed PV, and energy use by industrial subsector.
Demand response contributions are also tallied. BC Hydro applies a similar method to conduct
their long-term load forecasting (BC Hydro 2012). Of course, all electric utilities must perform
some type of load forecasting; Carvallo et al. (2017) describes the methods used by 11 utilities
and analyzes their performance in terms of historical forecast errors for annual energy and peakdemand. Three of the utilities studied used the SAE approach already reviewed, one used abottom-up engineering model, and the rest applied either timeseries or cross-sectional regression.

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Load Shape Models
Boßmann and Staffell (2015) describe two country-level models of future load shapes for
Europe, focusing on results for Germany and Britain. eLOAD 16 works by decomposing current
load shapes into portions associated with “relevant applications” and the remainder. Thus, end
uses of interest can be modeled differently than the remaining load, to which a basic scaling
factor may be applied. The end uses of interest are modeled in turn using the FORECAST
model, 17 which combines a stock accounting approach with stochastic simulation of technology
adoption decisions. DESSTinEE 18 is a spreadsheet simulation model grounded more in
macroeconomics; it models the demand for energy services as a function of population andincome. Load shapes are produced from a basic breakdown of current load by end use by
imposing the projected energy demands and weather (in the form of heating degree days) andforming a model of the residuals between historical load data and simulation results for thesame year.
1.1.3 Models of Electricity Load Flexibility
FERC Demand Response Potential Study
In a study related to load modeling, FERC (2009) estimated the potential percentage drop in peakload by customer class and state for three beyond-business-as-usual scenarios, which required
estimating the breakdown of peak load by sector and end use. Normalized load shapes bycustomer segment in the form of regression models over weather, air conditioning saturation, andperiodicity over multiple frequencies (season, month, day-of-week, hourly) were estimated fromutility hourly load data from 21 states. Temperature-dependent and non-temperature-dependent
loads were distinguished. The only end use modeled in some detail was air conditioning; evenso, there was no explicit data on the hourly load shape difference between customers with andwithout air conditioning. That difference was inferred by regressing over the utility load data andappliance saturation estimates that were available. These data and the methodology were
adjusted and applied to the Western Interconnection in Satchwell et al. (2013). Overall, this typeof demand response modeling can be characterized as relying heavily on utility reporting andsurveys concerning current demand response programs and load shapes overlaid with minimal
additional information on technology saturation levels.
Demand Response Estimates by Lawrence Berkeley National Laboratory
Work done by Lawrence Berkeley National Laboratory (Olsen et al. 2013) to estimate demand
response resource by sector and end use is in many ways the predecessor to dsgrid. Theirmethodology for constructing demand response resource curves starts with estimates of hourly
load by end use, constructed by merging many data sources. Geographically, these estimatesoriginally covered only the western United States, but they have since been extended to thewhole United States at the resolution of utility-state intersection (Hale, Stoll, and Novacheck2018). Commercial building loads are estimated using a model of the California commercial
building stock developed from California Commercial End-use Survey data. The load shapes arederived from building energy simulations across California, which obviously do not cover theeLOAD: electricity LOad curve ADjustment, http://reflex-project.eu/model-coupling/model-eload/
FORECAST: FORecasting Energy Consumption Analysis and Simulation Tool, “Methodology,”
http://www.forecast-model.eu/forecast-en/content/methodology.php
18
DESSTinEE: Demand for Energy Services, Supply and Transmission in Europe, https://wiki.openmodinitiative.org/wiki/DESSTinEE.
16
17

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full range of U.S. climates. Therefore, they are further adjusted using a linear regression relating
temperature variability to load variability, in addition to being scaled and offset to matchpredicted monthly energy consumption, when applied to other locations. Residential load by end
use is treated similarly. The underlying data set in that case is a residential end-use forecast fromthe California Energy Commission for 2020. Several other data sources are used to model
agricultural pumping, data center, municipal lighting, water pumping, refrigerated warehouse,and manufacturing loads.
In a series of reports, Lawrence Berkeley National Laboratory extends and applies their demand
response estimation methodologies to California (Alstone et al. 2016a, 2017). For that work, they
had access to extensive advanced metering infrastructure and demographic data at the customer
level from the three major investor-owned utilities in California. 19 The data were clustered, theload data were disaggregated by end use using first-order engineering models, and projections
were then made to the study years.
1.1.4 Sectoral Models
Many sector-specific models have been proposed to project electricity consumption at various
levels of spatial and temporal resolution. This includes is a vast body of literature on modelingbuilding electricity use, both for commercial and residential buildings. Transportation has beenstudied quite thoroughly, with different researchers tending to focus on the different subsectors,
led by light-duty vehicles, road shipment of freight, air travel, and public transportation. Publicly
available energy analysis of industrial subsectors and end uses has tended to be lesscomprehensive due to the incredibly diverse and business-sensitive nature of industrial activity.
In this section, we summarize notable efforts in each of these areas. In relation to dsgrid, we areinterested in (1) whether these models accurately capture energy use over large geographic
regions, provide sufficient spatial and temporal resolution to be useful in regional or national,
hourly or finer temporal resolution power system models, and (2) whether they can model futureenergy use based on capturing descriptions of technological change either exogenously or
endogenously. We generally find many efforts to be lacking in one or more of these areas, eachof which is necessary to couple sector-level energy models with power system models suitable
for renewable energy integration studies; but, one can imagine sector-level models being
extended to serve such a purpose.
1.1.4.1 Buildings Sector(Swan and Ugursal 2009) review building end-use energy consumption modeling techniques andcategorize the techniques as “top-down,” “bottom-up statistical,” or “bottom-up engineering”
models. These categories, along with positive and negative attributes of each, are described inTable 1, which was adapted from Swan and Ugursal (2009). In what follows, we focus on the
two types of bottom-up models: bottom-up statistical and bottom-up engineering, because thesebetter align with dsgrid’s needs for temporal resolution, sectoral resolution, and ability todescribe technological change. Bottom-up statistical models focus on describing the stochastic,
occupant-driven temporal variation in energy used for individual end uses, and they have mostly
been restricted to residential buildings based on the availability of, for example, time use surveys
Demographic data were available for all 11 million utility customers. The hourly load data set covers
300,000 customers.

19

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and metered data. In contrast, bottom-up engineering models focus on accurately capturingbuilding thermodynamics over typical weather conditions, which is accomplished by simulating
single-building energy use as an outcome of physical interactions between building equipment,
building envelope, and weather that are driven by deterministic occupant schedules (Crawley etal. 2001).
Table 1. Positive and Negative Attributes of the Three Major Building Energy
Modeling Approachesa

Focus

Top-Down

Bottom-Up Statistical

Sector-wide annual energy
use and trends

Stochastic occupant-driven Energy use driven by weather,
temporal variations in energy building physics, and occupantuse
schedules

Conducts long-term
Encompasses occupant
Positive
Attributes forecasting in the absence of behavior
any discontinuity

Bottom-Up Engineering

Models new technologies andpolicy implications

Includes macroeconomic
and socioeconomic effects

Imputes typical end-use
energy contribution

Determines each end-use
energy consumption by type,rating, etc.

Requires simple input
information

Includes macroeconomic
and socioeconomic effects

Determines end-use qualities
based on simulation

Encompasses trends
implicitly

Uses billing data and simple
survey information

Negative Relies on historical
Attributes consumption information

Is subject to multicollinearity

Assumes occupant behavior andunspecified end uses

Provides no explicit

Relies on historicalconsumption information

Requires detailed input
information

Provides coarse analysis

Requires large survey
sample to exploit variety

Is computationally intensive

representation of end uses

Does not incorporate
macroeconomic factors
a Adapted from Swan and Ugursal (2009)

Residential Buildings
Several proposed residential bottom-up models identify the contribution of each end use to theaggregate energy consumption profile of the residential sector. Capasso et al. (1994) developed abottom-up statistical model for evaluating the impact of demand side management on residential
customers, although this analysis did not include weather parameters. A bottom-up statisticalmodel of Finnish residential appliances by end use, with application to estimating potential
demand-side management effects is given by Paatero and Lund (2006). A similar effort for theUnited Kingdom, which was validated using a year’s worth of metered data from 22 homes, isdescribed by Richardson et al. (2010).

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Richardson, Thomson, and Infield (2008) introduce a Markov-chain technique to generate
synthetic occupancy patterns, based on survey data of people’s time-use in the United Kingdom.
The stochastic model maps occupant activity to appliance use, creating highly resolved synthetic
demand data. Widén and Wäckelgård (2010) follow a similar approach to relate residential
power demand to occupancy profiles in Sweden. Muratori et al. (2013) propose a model,
validated against metered data, to simulate hourly electricity demand of U.S. households, basedon a Markovian behavioral model calibrated using the American Time Use Survey. The resulting
profiles have been used to simulate residential demand response programs (Muratori and Rizzoni2016) and the impact of PEV charging (Muratori 2018). Johnson et al. (2014b) predict residential
power demand based on occupant behavior using a similar Markovian model also driven by theAmerican Time Use Survey data. Fischer, Härtl, and Wille-Haussmann (2015) describe detailed
modeling of residential electricity use for Germany, focusing on non-weather-dependent end
uses. The resulting hourly power profiles by end use were validated against metered data for 430
households. Wills, Beausoleil-Morrison, and Ugursal (2017) adapt and validate the Richardson
model to simulate Canadian residential appliance and lighting demands using 22 high-resolution,measured demand profiles from dwellings in Ottawa, Canada. Overall, these models demonstrate
the ability to capture the time-dependent nature of residential loads by end use using a
combination of metered data, occupant survey data, and a variety of statistical simulation
techniques. However, because they do not incorporate engineering models of multiple building
subsystems interacting with environmental conditions, they can only be used to model limited
shifts in technology or policy (e.g., changes that are limited to a single end use or are primarily
driven by a change in occupant schedules).
Early bottom-up engineering modeling efforts involved using 16 multifamily residential building
prototype models (Ritschard and Huang 1989) and 8 single-family residential prototypes(Ritschard, Hanford, and Sezgen 1992) in 16 climates to develop a database of hourly residentialbuilding loads. Huang, Hanford, and Yang (1999) updated and expanded the Ritschard et al.
(1992) prototypes to 112 single-family and 63 multifamily residential building prototypes, andthey used them to determine the contributions of various enclosure components and internal
gains to residential heating and cooling demand. Building on the earlier work by Ritschard et al.
(1992) and Huang et al. (1999), Hopkins et al. (2011) increased the number of prototypes by anorder of magnitude by simulating all 4,382 homes sampled for the 2005 RECS. Many importantenergy-related parameters, such as insulation levels, air tightness, and heating/cooling equipment
efficiency are not collected by RECS and thus had to be sampled from probability distributions.
ResStock, the latest research effort to model the U.S. single-family housing stock, uses 350,000
statistically sampled building models, drawing on building characteristics data from the 2009
RECS, the U.S. Census Bureau’s American Community Survey (ACS) and 10 other data sources(Wilson et al. 2016, 2017).
Commercial Buildings
Significant effort has been made to examine energy use in commercial buildings from a bottomup engineering perspective. Some models, such as those developed by Fonseca and Schlueter(2015) use simplified, algebraic hourly models to describe end uses in buildings. Others, such as
Yang, Li, and Augenbroe (2018); Heeren et al. (2013) use quasi steady-state approaches (ISO13790:2008: Energy Performance of Buildings – Calculation of Energy Use for Space Heating
and Cooling 2008, 137), which also avoid differential equation-based physical modeling. These
simplified models generally require few inputs and run quickly, making them attractive in high11
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level studies of the commercial building stock over long time scales (Yang, Li, and Augenbroe
2018). Other studies use whole-building energy modeling software that directly simulates zonal
or finer-scale thermodynamics and is resolved at the hourly or subhourly level (Heiple and Sailor2008); B. Griffith et al. 2007; Griffith et al. 2008; Sezgen et al. 1995; Coffey et al. 2015; Dirks et
al. 2015). These models promise greater accuracy at a cost of longer computational times andhigher input data requirements.
Another issue in commercial building stock modeling is the development of archetypes that
represent a larger segment in aggregate after weighting factors are applied to the simulation
results for each model. Reinhart and Davila (2016) use as few as 13 archetypical building models
to model commercial building energy use in Ireland, whereas 3,168 archetypical models havebeen used in the case of Italy to represent 877,144 commercial buildings. Similarly, U.S. analysis
is often done using a subset of the 17 DOE “reference building” (Deru et al. 2011) or “prototype
building” models (Goel et al. 2014), which, depending on the scope of the study in terms ofbuilding types and climate regions, can result in a few up to 272 archetype buildings (e.g., Hart
et al. (2015) uses 30, covering six building types and five climate zones). Dirks et al. (2015) use
approximately 2,000 archetypes to model the eastern half of the United States. As pointed out by
Brogger and Wittchen (2017), a challenge of the archetype approach is ensuring the archetypemodels are actually representative of the stock. To overcome this limitation with archetypemodeling, a different approach has been taken by Coffey et al. (2015) in which each building
within the defined stock is modeled using a combination of 3-D building geometry and highlevel information (e.g., floor area and use type). The approach required an expert system that wasused to create individual whole building energy models, which results in a requirement forsignificant input data that may not be available on a national scale while simultaneous greatly
expanding the size of the required simulation set.
When evaluating the magnitude of errors in multi-sector commercial analyses, Reinhart andDavila (2016) demonstrate that large-scale studies only match the energy consumption of theaggregate building stock within 1%–19% of site energy use on an annual basis. while notaddressing issues of time of use. Akbari et al. (1993); Sezgen et al. (1995) both attempt toaddress this by applying load shapes developed from additional data sources. Though thisapproach may be adequate for predicting future scenarios where the load shape is not expected tochange significantly, it is inadequate for predicting future scenarios where new technologies are
expected to change the load shape, which is the case when considering electrification of thebuilding stock. The Heiple and Sailor (2008) model is one of the few building stock models thathas been validated for both annual energy consumption and load shape. In this case, the load
shape for a tractable geographic extent was validated against top-down models of the city,
allowing for derivation of measured data for a larger entire region containing that city.
Most of the bottom-up, physics-based models for buildings represent occupant behavior by
applying schedules that dictate both the presence of occupants (e.g., occupant component ofthermal loads, and hot water use) and their impact on equipment controls (e.g., lighting systemstatus; heating, ventilation, and air conditioning [HVAC] control mode; and HVAC set points).
Many of these schedules are based on previous research into this topic. Heiple and Sailor (2008)
note that these schedules can noticeably impact the overall results, and they suggest creating
additional archetype divisions with different operating schedules. Kim and Srebric (2017); Chen,
Hong, and Luo (2018) examine data sources and methodologies to develop diverse and
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stochastic schedule sets for university buildings and offices respectively. This research is very
promising, and it will be an excellent candidate for inclusion in stock-wide modeling,
particularly as additional classifications of commercial buildings are considered in the literature.
Summary
Ideally, there would be building stock models covering large geographic extents informed byboth detailed stochastic modeling of occupant behavior (bottom-up statistical models) anddetailed modeling of building physics interacting with weather, occupant behavior, andequipment controls (bottom-up engineering models). However, the current state of the art is thatthese two types of modeling approaches are practiced separately. The occupant-focusedstatistical models (which are mostly restricted to the residential sector) typically have good
temporal and end-use resolution but a limited ability to model technological change because they
are stochastic simulations based on historical time-of-use-surveys and metered data. The physicsfocused engineering models (for both residential and commercial buildings) are well suited tomodel the energy impacts of technological change by end use, but they struggle with achieving
accurate temporal resolution. This shortcoming is primarily driven by the historical emphasis onwhole-building, annual energy efficiency metrics; a lack of hourly data by end use forcalibration; and the naturally wide variance in occupant behaviors and control settings that insome sense should be averaged out when the primary concern is creating energy efficient designs
for buildings that may be used by multiple occupants over the course of their designed lifetimes.
This report documents efforts to begin to calibrate bottom-up engineering building stock models(particularly ResStock and the newly minted ComStock) on an hourly or subhourly basis. We
expect to see more such efforts given the increasingly urgent need to describe both timedependence of energy use and the potential impacts of technological change.
1.1.4.2 Transportation Sector
Electricity load modeling for the transportation sector, especially as it focuses on the adoption
and charging profiles of passenger PEVs, has been proposed by many researchers in response to
the potential for these vehicles to capture significant market share. Though some studies,
especially those with a focus on climate change mitigation, also examine demand trends in othertransportation subsectors (Garrido and Mahmassani 2000; Kamakate and Schipper 2009; Eom,
Schipper, and Thompson 2012; Muratori et al. 2017; Winchester et al. 2013; Boeing 2015;
Paulley et al. 2006), significant electricity consumption is not typically projected for thosesubsectors due to a lack of mature technological options (e.g., electric air or marine transport).
Several vehicle-to-grid studies leverage travel information surveys to predict PEV charging
demand (Clement-Nyns, Haesen, and Driesen 2010; Denholm and Short 2006; Duvall et al.
2007; Green, Wang, and Alam 2011). Bashash and Fathy (2012), for example, propose a controloriented model representing the collective charging dynamics of PEVs that uses the U.S.
National Household Travel Survey to predict the number of PEVs connected to the grid at any
given time. Other studies use behavioral models to predict vehicle use and charging patterns(Muratori, Moran, et al. 2013) or queuing theory to estimate the overall charging demand of a
population of PEVs (Li and Zhang 2012). Ashtari et al. (2012) instrumented 76 PEVs in
Winnipeg, Canada, to record vehicle driving and parking patterns. One-second charging profiles
were used to calibrate a stochastic method to predict PEV charging loads and capture the13This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

relationship of vehicle departure, arrival, and travel time for different charging scenarios (e.g.,
residential versus workplace and different charging power levels).
In this context, NREL, in partnership with the California Energy Commission, developed theElectric Vehicle Infrastructure Projection (EVI-Pro) model 20 to simulate spatially and temporally
resolved demand for PEV charging at residential, workplace, and public destinations based onreal-world travel data (Wood et al. 2017). EVI-Pro anticipates consumer charging behavior
while capturing variations with respect to housing type (single-unit versus multiunit dwellings),
travel period (weekdays versus weekends), and regional differences in travel behavior andvehicle adoption.
1.1.4.3 Industrial Sector
Given the highly complex, heterogeneous, and proprietary nature of the industrial sector, and acorresponding lack of comprehensive data sources, detailed models of its energy consumption
have lagged models of other sectors. In past studies, top-down estimates based on projections
from the EIA AEO, which is produced annually using NEMS, have been common (Hostick et al.
2012; Holmes et al. 2014). Though these studies provide a good baseline of aggregate energy
consumption within the industrial sector, their granularity is typically limited to the census
division level.
In addition to using AEO data, many studies incorporate data from EIA’s MECS. This national
survey is conducted every four years and collects information on U.S. manufacturing
establishments, their energy-related building characteristics, and their energy consumption and
expenditures (EIA 2017a). Within the MECS, detailed estimates of energy consumption byindustry type and census region are available at the end-use level. Additionally, the DOE
Advanced Manufacturing Office has used these data in numerous studies of specificmanufacturing industries. 21
Though these studies provide useful information for the individual industries covered, they
demonstrate the primary issue in modeling industrial sector energy use, which is that there is no“one size fits all” bottom-up modeling approach. Although various industries may share similar
processes and end uses, their energy consumption may vary greatly depending on what type of
product is being produced, and no one data source describes process units consistently across allindustrial manufacturing subsectors. As a result, detailed technology level models are rare,
limiting the granularity that can be achieved by an industrial sector model.

1.2 Scope of dsgrid

dsgrid is a model of U.S. electricity use with an unprecedented combination of temporal,
geographic, and sectoral detail and coverage (Figure 1). Previous load models focused on similar
questions have reasonably focused on (1) seasonal and peak load effects and (2) building end
uses that are widespread and either (a) large as a proportion of peak load with the potential to becontrolled (for demand response studies) or (b) with significant energy efficiency potential.

EVI-Pro: Electric Vehicle Infrastructure Projection Tool, https://www.afdc.energy.gov/evi-pro-lite
The so-called Bandwidth Studies are listed at “Energy Analysis by Sector,” DOE,
https://energy.gov/eere/amo/energy-analysis-sector.
20
21

14
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A recent effort to develop more end-use and geographic specificity (Olsen et al. 2013) isleveraged here, but what the report describes is fundamentally different in scope.
dsgrid uses large-scale building energy modeling and gridded meteorological data to accuratelyreflect regional differences in equipment types as well as weather to enable realistic
multidimensional “what-if” analyses. It also provides hourly subsectoral detail on industrial
manufacturing loads and captures other important non-building loads. Projections of future load
will include detailed modeling of PEVs, both passenger and commercial vehicles, coupled withmultiple charging strategy and scheduling flexibility assumptions.

Figure 1. Load models summarized by their geographic, temporal, and sectoral extents
and resolution
dsgrid provides an unprecedentedly detailed picture of current and, when coupled with EnergyPATHWAYS,
future continental United States (CONUS) electricity load

In the remainder of this report, the authors describe dsgrid: its modeling approach and initialbaseline results for one historical year. Future work will use outputs from theEnergyPATHWAYS model to develop detailed descriptions of future load scenarios.
The dsgrid data sets and model are a timely effort to understand the time-varying nature ofelectrical demand and flexibility at an unprecedented level of detail that will be used to explore
future scenarios of the U.S. electric sector.

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2 Model Architecture: A Composite Picture Built Upfrom Sector Models

So that it can provide the necessary sector level details and compute residuals against historicalelectricity-sector data, dsgrid was not developed as a single model but rather as a confederationof models and data sets bound by processes for comparing and harmonizing data across several
levels of geographic, temporal, and sectoral resolution.
In this section, the authors summarize the model as a whole and its constituent parts. We firstprovide an overall architectural view (Section 2.1). This is followed by brief descriptions of thesector models, which are provided in two “flavors”: (1) detailed, bottom-up models whose data
are available at hourly resolution by subsector, end use, and county, and (2) gap models that rely
on a mixture of top-down and bottom-up data sources and are typically coarser than the detailed
models in at least one dimension (Section 2.2). After summarizing the sector-level modeling as awhole (Section 2.2.5), we describe two additional gap models, models of distributed generation,and derived data sets describing system losses and model residuals (Section 2.3).

2.1 Architectural Overview

The heart of the dsgrid model is the bottom-up detailed sector modeling described briefly here
and in more detail in Section 2.2 and Appendices A, B, and C. However, to create acomprehensive picture of U.S. electrical load, the model must also account for other aspects ofsystem load, such as loads not captured in detail by the sector models, distributed generation, andT&D losses; and should also be calibrated and validated against historical data. To accomplishthese goals the detailed bottom-up sector models are complemented by coarser gap models,
distributed generation models, and several historical electric-sector data sets within an analytic
framework that allows comparison and computation across different levels of geographic,
temporal, and sectoral resolution (Figure 2, next page).
The detailed bottom-up energy modeling for each sector is conducted with separate
methodologies, following the overall philosophy of leveraging and supporting the energy
modeling work conducted by research groups focused on particular end-use sectors, rather thanattempting to recreate or repurpose such work from a pure power systems point of view. The
scope and methods used for each sector model are thus products of prior work done by eachsector modeling team combined with modifications necessary to meet the temporal andgeographic resolution required by dsgrid. Brief descriptions of the methodologies follow:•

Residential and Commercial Buildings (NREL): Building loads are estimated using
ResStock and ComStock, which use similar statistical methodologies and OpenStudiomodeling infrastructure to simulate U.S. single-family detached and commercial building
stock electricity consumption by end-use. These models sample from thousands ofprobability distributions to produce hundreds of thousands of EnergyPlus simulations,which are then weighted to represent subsector building stocks at the county level. Thisdetailed modeling covers single family homes and commercial buildings mappable to the
16 DOE commercial “prototype buildings” (Goel et al. 2014).

•

Industrial Manufacturing (EPRI/ORNL): Industrial manufacturing loads are modeledwith the Industrial Geospatial Analysis Tool for Energy Evaluation (IGATE-E), which
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uses plant-level databases, the Manufacturing Energy Consumption Survey, and theElectric Power Research Institute’s Load Shape Library to construct hourly time series ofelectricity use by manufacturing subsector and end use. Because manufacturing processes
vary greatly, IGATE-E does not attempt direct simulation of loads but rather compiles
data from multiple sources and applies statistical techniques to estimate energy
consumption down to the end-use level. Because IGATE-E only models manufacturing,
the additional industrial sectors of agriculture, mining, and construction comprise theindustrial gap model.

Figure 2. dsgrid input data architecture
CHP = combined head and power

FERC = Federal Energy Regulatory Commission

DPV = distributed solar photovoltaics

IGATE-E = Industrial Geospatial Tool for Energy
Evaluation

EIA = U.S. Energy Information Administration

ISO= Independent System Operator

EVI-Pro = Electric Vehicle Infrastructure
Projection Tool•

SERA = Scenario Evaluation, Regionalization andAnalysis modelTransportation (NREL): Given the focus in this report on constructing a model ofhistorical electricity use we describe the detailed transportation modeling methods thatwill be used in future work to capture EV location and charging; but no detailed sector17

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level results for transportation are presented. When this capability is developed for the
EFS future model year snapshots, on-road plug-in electric vehicle operation will be
described using the Scenario Evaluation and Regional Analysis (SERA) model and theElectric Vehicle Infrastructure Projection Tool (EVI-Pro). SERA describes vehicle
infrastructure requirements and will be used to disaggregate vehicle adoption to thecounty level. EVI-Pro simulates hourly charging profiles based on travel data andcharging preference assumptions (e.g., residential charging as opposed to reliance on
public charging). The historical data set includes a transportation gap model that
describes electricity use in passenger trains.
For the initial historical snapshot described here, the year being modeled by dsgrid is 2012. This
choice is based on data availability, including meteorological data that impacts some of thesector models and ultimately needs to be time synchronized with the renewable generation data
to be used in the electricity supply modeling portion of EFS. The current baseline years for thesector models are•

Residential Buildings: 2009 RECS and 2010–2014 ACS•

Commercial Buildings: 2012 CBECS•

Industry: 2014 MECS•

Transportation: 2012 National Transit Database

The sector models that are not primarily calibrated to 2012-specific data sets are scaled torepresent the historical 2012 model year. For example, for the residential sector, the 2010–2014
ACS provides dwelling unit counts by census tract, but when calibrating against 2009 RECS
energy consumption data, the 2009 RECS dwelling unit counts are scaled up to match theoccupied dwelling unit counts from the 2010–2014 ACS for each building type and census
division. Industry scaling from 2014 back to 2012 is done using the EIA SEDS data, which show
about a 1% increase in industrial retail electricity use over that time (EIA 2017b).
Moving up from the bottom of Figure 2, the dsgrid gap models capture (1) residential,commercial, industrial, and transport subsectors that are not handled in our detailed sectoralmodeling and (2) municipal/utility end uses such as municipal lighting, and water pumping andtreatment. The magnitude of most of these gaps is first estimated using data products from the
EIA and other federal agencies, namely, the EIA Residential Energy Consumption Survey(RECS), the Commercial Building Energy Consumption Survey (CBECS), the AEO, theNational Transit Database (NTD), and the County Business Patterns data (EIA 2013d, 2016a,
2015a; FTA 2017; U.S. Census Bureau 2018). We then form our gap model by assigning a proxytimeseries to each of the gap magnitudes identified in those data sets. For some subsectors wealso geographically downscale our estimates using additional data sources available from thebottom-up models. The processes and specific assumptions made for each gap are described in
Section 2.2 (sectoral gap models) and in Section 2.3.1 (municipal end uses). The temporal
resolutions of the gap energy magnitude data are uniformly annual. The geographic and sectoral
resolutions vary. Some of the key data are only available at the census division level or for thenation as a whole, but in all cases, we can geographically downscale at least to the state, andsometimes to the county.

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To approximate total site-level grid-delivered electricity use by sector and state, the authors
estimate hourly distributed generation from customer-sited PV, combined heat-and-power(CHP), and distributed thermal plants, and subtract this from the sum of the detailed and gap
bottom-up load models. The distributed generation models are distinguished by sector(residential, commercial, or industrial) to facilitate annual by-sector, by-state comparisons tohistorical by-sector electricity demand. The hourly profiles, in addition to facilitating the creationof grid-delivered electricity profiles, allow for hourly by-state comparisons to historical total
electricity demand. The main data sources for our distributed PV (DPV) model are Perea et al.
(2017) and Sigrin et al. (2016). CHP and distributed thermal capacity, generation, and behindthe-meter fractions are estimated using EIA Forms 860 and 923 (EIA 2013a, 2018), and the DOE
CHP Database (ICF Inc. and DOE 2016). CHP and distributed thermal hourly profiles are
generated from continuous emission monitoring systems (CEMS) data (US EPA 2016; EPA
2013).
To calibrate and validate the initial dsgrid data set for historical year 2012, we leverage two
historical electricity-sector data sets. Shown at the top of Figure 2, with no sectoral resolution
and coarse geographic resolution, we have a historical data set of hourly electrical load that iscomprised of independent system operator (ISO) data, and FERC Form 714 filings (FERC 2016;
SPP 2016; pjm 2016; MISO 2016; ISO New England 2016; NYISO 2016). The native resolutionof that data is at the utility, balancing authority, or ISO region level. For this version of dsgrid we
use a version of the data set that has first been disaggregated down to individual transmission
nodes, and then re-aggregated up to the state level. In future versions we plan to enable analysis
at the county or utility level by fully leveraging the nodal disaggregations, but it was not
tractable to achieve a reliable geographic matching at that level of detail within the scope of thisinitial project. This load data represents the system operator perspective, that is, it is equal togeneration plus imports minus exports, and is thus the total amount of generation plus net
imports that the system had to obtain to meet its load and cover all T&D losses.
Annual retail sales data are available from EIA Form 861 (EIA 2013b), where they are reported
by sector (i.e., residential, commercial, industrial, and transport 22) for every utility by state. EIA
Form 861 also reports the amount of electricity furnished or consumed by respondent withoutcharge, which we add to the commercial retail sales. These data—electricity consumption by
sector and state—represent the total amount of electricity sold or furnished to utility customers,
and thus excludes system losses. These data are more resolved sectorally and geographically thanare the top-down hourly load data, but they are temporally coarser. EIA Form 861 also reports
losses by utility and state, and this is the main data source for our loss model.
In Section 2.3, in addition to further describing the gap and distributed generation models, theauthors describe how we estimate system losses and compute model residuals. Hourly systemlosses are estimated by combining the historical hourly load, historical annual sales, andhistorical system losses data. Model residuals are estimated by combining the historical data sets,

In the coming decades, we expect significant amounts of electricity used to charge EVs to show up in EIA filings
as residential, commercial or industrial, rather than as transportation, electricity use based on behind-the-metercharging at residences and commercial or industrial sites.
22

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the hourly system losses model, and the dsgrid components—detailed bottom-up sector models,
gap models, and distributed generation models.

2.2 Sector Models

The core of dsgrid leverages previous work by four energy modeling teams, each of which brings
multiple large-scale data sets together to form a holistic picture of an individual sector:
residential buildings, commercial buildings, industrial manufacturing, and transportation. The
four modeling teams have deep institutional experience estimating the energy use of their various
sectors, although usually on an annual or seasonal, rather than hourly basis. By expanding the
temporal, and in some cases, geographic resolution of these energy-use estimates, harmonizing
assumptions, and undertaking a collaborative calibration effort, dsgrid creates a highly resolved
picture of United States electricity use.
2.2.1 Residential Sector
ResStock is a bottom-up simulation methodology for modeling residential building stocks with ahigh degree of granularity. In development by NREL since 2013 (Wilson et al. 2016, 2017),
ResStock characterizes the energy use of U.S. single-family detached housing 23 using ahierarchical structure of conditional probability tables and detailed energy simulations ofhundreds of thousands of representative buildings.
2.2.1.1 Input Data Sets
The conditional probability distributions are synthesized from data queried, translated,aggregated, and extrapolated from 11 sources, including U.S. census data, RECS, builder
surveys, and other data from field studies. These data are supplemented by engineering estimates
where data are lacking. Details of the housing stock characterization data model can be found in
Wilson et al. (2017). A summary of the input data sources is shown in Table 2. The data sources
are described in detail in Appendix A.

ResStock is currently limited to single-family detached housing. Capabilities for low-rise multifamily and mobile
homes are under development.
23

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Table 2. Building Characteristics, Dependencies, and Data Sources

Meta

Location
Vintage
Heating fuelUsage levelDaytime use
Geometry Floor area
Number of stories
Foundation type
Attached garage
Orientation
Envelope
Window type
Wall insulation
Attic insulation
Foundation insulation
Air leakage
Equipment Heating system typeHeating system efficiency
Cooling system type
Cooling system efficiency
Duct insulation, tightness
DHW system type
DHW system efficiency
Cooking type
Clothes dryer type

Number of Options

Geographic Resolution

Eng. Exp. & Calibration

Lucas and Cole 2009

Wenzel et al. 1997

Chan et al. 2012

Labs et al. 1988

ACS

Ritschard et al. 1992

RBSA (NEEA 2012)

IECC 2009

NAHB

2009 RECS (EIA 2012)

Found. Type

Number of Stories

Floor Area

Data Sources

Daytime Use

Usage Level

Heating FuelCharacteristics

Vintage

Location

Dependencies

TMY3 216
•

C

7

C

6

U.S.

3

U.S.

2

•

R

6

•

R

3

48

5




•



•












•






•

•



•

•









•





•

































•
•
•

•




•


•



•

•

•
•

5

R

8

R

7
5

R

12

R

6

R

10

R

7

•

R

7

•

U.S.

5

R

5

•

U.S.

3

•



R

R

•

•
•

4

•

•


2

•

•
•

R
U.S.







•

R

10







•

R

10

21
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Number of Options

Geographic Resolution

Eng. Exp. & Calibration

Lucas and Cole 2009

Wenzel et al. 1997

Chan et al. 2012

Labs et al. 1988

ACS

Ritschard et al. 1992

RBSA (NEEA 2012)

IECC 2009

NAHB

2009 RECS (EIA 2012)

Found. Type

Number of Stories

Floor Area

Data Sources

Daytime Use

Usage Level

Heating FuelVintage

Characteristics

Location

Dependencies•
TMY3 3
Occupancy Heating, cooling set points


•
U.S.
3
Cooking usage


•
U.S.
3
Clothes dryer usage


•
•
U.S.
3
Lighting, appliances, MELs
 = direct dependency  = indirect dependency
italics = archetype parameters
MELs = miscellaneous electric loads
C = Census Tract
R = Regional (custom)
TMY3 = 216 typical meteorological year subregions
NAHB = National Association of Home Builders
ResStock statistically represents housing stock characteristics with 6,000 conditional probability distributions derived from 11 data sources. This table provides
information on how each parameter’s probability distributions depend on other archetype parameters, as well as each parameter’s data sources, geographic
resolution, and number of options (bins).

22
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ResStock also depends on geospatial weather data. It uses 216 climate subregions as the coregeographic resolution for its building energy simulations (see Figure 3). The 216 subregions are
clusters of approximately 84,000 National Solar Radiation Data Base 10-km2 grid cells that aregrouped based on proximity, elevation, and data similarity, using a method described in Lopez(2011). For this work, 216 EnergyPlus weather (EPW) files, one for each climate subregion’s
representative location, was assembled using National Solar Radiation Database (NSRDB) datafrom 2012 as described in Appendix H.
To achieve consistency with the commercial, industrial, and transportation sector components ofdsgrid, ResStock results are mapped from the 216 TMY3 locations to the 3,107 counties in the
CONUS. This mapping is done using census-tract-level household data from the 2012 ACS
combined with a geospatial filter that excludes non-residential land. The filter is derived froma 200-m residential land mask computed from LandScan Nighttime and Daytime Gridded
Population data (ORNL 2011) and Homeland Security Infrastructure Program facility location
data (HSIP 2012). This mapping uses iterative proportional fitting to mesh data from the ACS
Public Use Microdata and the American Housing Survey to ultimately account for the
distribution of building types, vintage, heating fuel, cooling type, floor area, and householdincome within census tracts. Aggregated timeseries results for each county are standardized toEastern Standard Time (EST) to synchronize electrical load profiles across all U.S. time zones.

Figure 3. ResStock simulations use 216 climate subregions, each represented by a single
weather station.

23
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2.2.1.2 Methodology
The data sets described above are used to create a hierarchy of thousands of conditional
probability tables that collectively define more than 100 building components. Statisticalsampling based on a modified Latin hypercube sampling approach is used to selectrepresentative homes from the parameter space defined by the housing stock data model (Wilson
et al. 2016). Detailed subhourly building energy simulations modeling a year’s worth ofoperations are assembled using OpenStudio and run in EnergyPlus (Roth, Goldwasser, andParker 2016; Crawley et al. 2000). For this project, the energy consumption of the single-family
housing stock of the CONUS is represented by 350,000 building/location models run on
Peregrine, NREL’s high-performance computer, or distributed cloud computing.
Convergence testing of simulation results sliced various ways led us to select 350,000 as thenumber of building/location models (combinations of building characteristics and climate
locations) to represent the current U.S. housing stock. Weighting factors are used to scale results
up from 350,000 to the 73.2 million regularly occupied single-family detached homes includedin the analysis, based on the 2010–2014 ACS (Manson et al. 2017; U.S. Census Bureau 2017).
2.2.1.3 Output Data
Each EnergyPlus simulation produces timeseries results of whole-building electricity and fueluse, as well as electricity and fuel use by end use. ResStock simulations typically use ten-minutetimesteps for zone calculations with smaller timesteps used for HVAC system calculation on an
as-needed basis. Timeseries results can be reported with ten-minute resolution but are aggregatedto hourly resolution for dsgrid. The end uses reported for dsgrid ResStock analysis are based onstandard EnergyPlus reportable meters, and the dsgrid bottom-up residential data set reports
hourly timeseries of:•Fans: electricity (kWh)•Pumps: electricity (kWh)•Heating: electricity (kWh)•Cooling: electricity (kWh)•Interior lights: electricity (kWh)•Exterior lights: electricity (kWh)•Water systems: electricity (kWh)•Interior equipment: electricity (kWh)•Heating: gas (kBtu)•

Water systems: gas (kBtu)•

Interior equipment: gas (kBtu)•

Water systems: water (gal)24This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Customizable OpenStudio scripts can be applied to report consumption for more detailed end
uses, such as plug loads, cooking, and clothes drying. Additional reports related to building
performance (e.g., relative humidity and zone temperatures) can also be generated.
The output timeseries are provided at the county level and are time-synchronous with 2012
meteorological data, based on the geospatial disaggregations and weather data transformations
described above.
2.2.1.4 Calibration
ResStock, and building energy modeling in general, have previously focused on predicted annual
energy savings results for applications related to energy efficiency in buildings. ResStock iscurrently calibrated by comparing modeled energy use by fuel type to the corresponding energy
use metrics given in the 2009 RECS (EIA 2013d) for slices of the housing stock, such as region,
vintage, and space heating fuel type. Iterative changes to model inputs were made to bringmodeled consumption into better agreement with the reference consumption. The use of
ResStock for producing electric load profiles under various scenarios is being validated as part ofthis effort. Models for bottom-up load modeling can generally be categorized as models thatgenerate either (1) aggregated load profiles or (2) agent load profiles for individual buildings orappliances. Individual building agent load profiles reflect the stochastic behavior of event-basedload spikes (e.g., toaster on for five minutes), which when summed over hundreds or thousandsof buildings, approach the smoothness of aggregated load profiles needed for bulk power systemanalysis. Initially, dsgrid and ResStock focus on the former category of aggregated load profiles,
though the latter category of agent load profiles is of interest for other applications. 24
In 2016, NREL completed initial validation of the non-weather dependent end use load profiles(e.g., appliances, plug loads) generated by ResStock. In 2017, the validation effort focused on theweather-dependent end uses (e.g., space heating, space cooling, and domestic water heating).
Sources of measured electric load profiles being used for validating ResStock include:•

24

Utility Load Research Data (ULRD): Electric utility companies collect load profile
data from a sample of their customers for various internal and external uses. NREL has
obtained a collection of ULRD for one or more years from about 30 U.S. electric utilities.
The hourly load profiles are typically aggregated by customer class; residential customers
that use electricity for space heating are sometimes split into a separate “space heat”
class. Commonwealth Edison (ComEd) is one utility that splits ULRD into single-family
and multifamily classes as well. Thus, the ComEd ULRD is particularly useful for
validating ResStock single-family load profiles. Figure 4 summarizes some of theresidential data in this data set, which is clearly summer peaking.

Though ResStock models individual buildings, it cannot yet model event-based load spikes for most end uses.

25
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Figure 4. Average of residential load profiles from eight electric utilities available in the
Utility Load Research Data (ULRD)•End Use Submetering Studies: These studies measure the load profiles of individual
end uses (e.g., heating, cooling, water heating, appliances, and miscellaneous plug loads)
within individual buildings. The data collection is usually expensive and intrusive tobuilding occupants, so submetering studies are uncommon and typically limited in termsof number of homes, geographic scope, and representativeness. The Residential Building
Stock Assessment Metering (RBSAM) study, which recorded data at 15-minute intervals
for over 150 end uses in about 100 single-family homes in the Pacific Northwest (Larson
et al. 2014), is the primary submetering study used for ResStock validation. Notablechanges made to ResStock inputs based on comparing ResStock outputs to the RBSAM
data set are shown in Figure 5, Figure 6, and Figure 7. Additional plots showing
preliminary calibration of weather-dependent end uses are included in Appendix A.

Figure 5. The ResStock plug load schedule was updated to use the measured plug load profile
from RBSAM (mid-day valley eliminated).

26
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Figure 6. The ResStock lighting schedule algorithm was updated to eliminate the deep mid-day
valley and more closely match measured lighting use from RBSAM.
The algorithm is a piecewise function with six components that are functions of building latitude, season, andsunrise/sunset times.

Figure 7. After modification, the ResStock lighting schedule algorithm results in an improved
match to the RBSAM data for all months of the year.

27
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Other model improvements include addressing unrealistic coincidence in occupant behavior
regarding exhaust ventilation and hot water use events. For exhaust ventilation (e.g., bathroom,
kitchen, and clothes dryer exhaust), the model was changed from simulating every home having
exhaust ventilation events at the same time to using a probability distribution to assign timing of
the exhaust events. This had the effect of spreading spot ventilation out over the day realisticallyfor the sample of homes modeled in ResStock. For hot water use, a change was made to use
1,615 repeating weeks for the draw profile instead of a set of 50 annual draw profiles. This led
to significantly more diversity in hot water draw patterns across homes, and it reduced theoccurrence of unrealistically coincident water heater electricity demand.
2.2.1.5 Subsector Gap Model
When ResStock was initially developed, NREL’s residential modeling algorithms were focusedon single-family detached housing, based on a history of developing energy modeling andoptimization tools for research involving large, production homebuilders in the U.S. Departmentof Energy (DOE) Building America Program (Christensen et al. 2006; Wilson et al. 2014).
Consequently, ResStock currently does not represent multifamily housing, including duplexes,
and low-, mid-, and high-rise residential buildings. 25 Mobile homes are also not currently
represented. Larger multifamily buildings, in particular, mid- and high-rise residential buildings,
are represented as part of the commercial building stock (Deru et al. 2011). To facilitatecomparison to EIA Form 861 residential data (EIA 2013c), the dsgrid detailed residential sector
model is ultimately comprised of the ComStock mid- and high-rise apartment building
subsectors sitting alongside the ResStock results in a single data file.
The remaining residential subsectors form the residential gap model (Table 3). The energy use
for these gap subsectors is approximated by scaling the RECS 2009 energy use by the numberof dwelling units reported in the 2010–2014 ACS divided by the number of dwelling units in
RECS 2009. These factors are computed by subsector for the 27 reportable domains in RECS
2009 (large states and groups of states) (Table 4). The energy use is then distributed to counties
based on the 2010–2014 ACS occupied-only housing unit counts, again, on a residence-typebasis.
Table 3. Residential Subsectoral Gaps: Size and Proxy Timeseries Description

Subsector

Est. Portion of CONUS
Residential Electricity
Description
Use (%)

Proxy Timeseries
Geographic
Resolution

End-UseResolution

Mobile homes

7.2

Single-family
detached (1,000 ft2
size bin)

State

Same asResStockLow-rise apartments

5.0

Mid-rise apartments State(ComStock)

Same asComStockSingle-family attached(duplexes andtownhomes)

4.7

Single-family
detached(1,000 ft2 size bin)Same asResStock

25

State

ResStock multifamily capabilities are under development as of 2018.

28
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The proxy timeseries for the residential gap model are also summarized in Table 3. As can beseen in the table, low-rise apartments use the mid-rise apartment load timeseries, broken down
by end use, from ComStock; and the other two gap subsectors, single-family attached and mobile
homes, use timeseries from ResStock, not the full county-level data, but the portion describing
homes smaller than 1,500 ft2. In all cases, for each building type and county, if a county-levelproxy timeseries is available, it is used. If no proxy timeseries is available at the county level, thestate-level aggregate is used. In all cases, the proxy timeseries are scaled to match the electricity
use (megawatt hours [MWh]) per building type and county estimated based on RECS 2009(energy use) and the 2010–2014 ACS (energy use scaled to 2012 using Table 4, and downscaled
to counties based on relative building counts).
Table 4. Residential Scaling Factors: 2010–2014 ACS Occupied Dwelling Units Divided by RECS
2009 Dwelling Units, by Building Type and Census Division
Apartments inFive5 or More
Unit Buildings

Mobile Homes

98%

100%

108%

84%

135%

106%

116%

99%

105%

103%

106%

93%

100%

85%

160%

138%

89%

105%

92%

100%

97%

134%

106%

112%

96%

99%

94%

102%

133%

103%

110%

83%

102%

101%

116%

113%

72%

107%

58%

89%

103%

121%

Census Division

Single Family
Detached

Single Family
Attached

East North Central

100%

109%

East South Central

96%

Middle Atlantic
Mountain
New England
Pacific
South AtlanticWest North Central
West South Central

Apartments in 2
to 4 Unit
Buildings

2.2.2 Commercial Sector
ComStock is a bottom-up, physics-based model developed for this project by NREL to capture
energy-related characteristics and model energy use of the U.S. commercial building stock. The
ComStock methodology builds on the framework established by ResStock (Wilson et al. 2017)
but differs in several ways guided by the unique physical and operational characteristics of, as
well as the data available for, commercial buildings. Primary differences include how thegeospatial distribution of buildings is calculated and how empirical data are translated into
building energy model inputs. ComStock is described here in brief. Detailed information on thedata, process, and assumptions used to develop ComStock are available in Appendix B.
The ComStock model is comprised of five parts:•

A database of commercial building characteristics•

Conditional probability tables synthesized from these data

29
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

•

A sampling methodology implemented in the Parametric Analysis Tool 26•

Model articulation using the OpenStudio software development kit 27 to create statistically
representative models; the programmatic re-creation and customization of DOE prototype
buildings 28 within this framework 29 is a key building block.

•

The EnergyPlus building energy simulation engine. 30

ComStock results consist of scaled, statistically representative building load profiles for the
CONUS commercial building stock with county-level granularity. The modeling process
is modular and highly flexible, such that alternate input data sets can easily be substituted andvarious OpenStudio scripts (termed “measures”) can be inserted into the workflow. Thus,
ComStock can be used to explore alternate geographies or years, as well as energy efficiency or
demand response measures that model alternate design or operational decisions.
2.2.2.1 Input Data Sets
We initiate the ComStock workflow with a hierarchical structure of conditional probability tables
for nine major determinants of commercial building energy use: building location (i.e., county),
building type, floor-to-ceiling height, building energy code, primary HVAC system, number offloors, total floor area, and vintage. We synthesize conditional probability tables with binned
values for each of these building characteristics (with the exception of energy code) fromnational data sets, namely CBECS (EIA 2016a) and CoStar, which is a building-levelcommercial real estate inventory from which we derive aggregates at the census block level(CoStar 2017). The resulting probability tables distill critical building characteristics atappropriate geo-spatial extents, for instance indicating that about 74% of primary schools in the
United States are one story and 15% have two stories.

For information about the OpenStudio Parametric Analysis Tool, see www.openstudio.net and the ParametricAnalysis Tool 2.1.0 (PAT) Interface Guide at http://nrel.github.io/OpenStudio-userdocumentation/reference/parametric_analysis_tool_2/.
27
For information about the software development kit, see https://www.openstudio.net/developers.
28
For information about DOE prototype models, see the U.S. Department of Energy Building Energy Codes
Program, “Commercial Prototype Building Models,” https://www.energycodes.gov/development/commercial/prototype_models, last updated April 14, 2016.
29
For information about the OpenStudio-Standards Gem framework, see https://github.com/NREL/openstudiostandards.
30
For information about EnergyPlus, see energyplus.net.
26

30
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Each building is assumed to meet the minimum energy efficiency requirements of either
ASHRAE Standard 90.1 (ASHRAE 2016) or the DOE “reference buildings” pre-2004
assumptions (Deru et al. 2011). We assume building codes are commensurate with building
vintage (e.g., if a building was constructed in 1994, we assume it adheres to the 1980–2004
ASHRAE 90.1 standards). In future work, we will derive state- and county-specific code
compliance from the Building Codes Assistance Project database and more-granular code
adoption data derived from municipality and county data-sources (BCAP 2017). We may also
use the last retrofit year field in the CoStar data to capture the proportion of older buildings thatshould now be considered compliant with newer building codes based on having been deeply
renovated. Additional building characteristics are derived primarily from building type and code
set using defaults specified in the DOE prototype buildings (Goel et al. 2014) and the NREL
Sector Model (B. Griffith et al. 2007), and are subsequently maintained by NREL in the
OpenStudio suite of modeling tools. 31 The major building characteristics captured by ComStock,
their interdependencies, and data sources are summarized in Table 5.

31

See Footnote 29.

31This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table 5. Building Characteristics, Dependencies, Data Sources, and Number of Variable Values
Used in the ComStock Modeling Process

Meta

X
X

DOE prototype
building
Vintage
Energy code

X
X

Weather data

# of Options (bins) if
table exists

NREL NSRDB

X

N/Ab

X

102
X

X
X

X

17

X

10

X

6

Space typebreakdowna

X
X

X

Rotationc

X

8

Number offloors

X

X

X

16

Area

X

X

X

10

Floor-to-ceilingheightX

X

Building shape

X

46
12

Aspect ratiod

Internal
Loads

NREL Sector Model

9

CoStar building
type

Envelope

DOE Prototype
Buildings

Division
Location(county)

Geometry

CoStar (2017)

RECS (EIA 2009)

CBECS (EIA 2012)

Building Shape

Number of Floors

Vintage

Data Sources

DOE Prototype
Building

CoStar Building Type

Location (county)

Characteristics

Division

Dependencies

X

X

Window-to-wallratioaX

X

Construction
typea

X

X

Wall propertiesa

X

X

Windows
propertiesa

X

X

Peoplea

X

LightsaX

Plug loadsa

X

X

X
X

X

X

32
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6

X

X

X

Showers, sinks,
etc.a

X

X

Laundrya

X

X

Schedules

Operationschedulesa

X

HVAC

HVAC system
typee

X

# of Options (bins) if
table exists

Kitchenequipmenta

NREL NSRDBXNREL Sector ModelX

CoStar (2017)

X

Service
Water
Heating

DOE Prototype
Buildings

RECS (EIA 2009)

CBECS (EIA 2012)

Building ShapeElevatorsa

Characteristics

Division

Vintage

Number of Floors

Data Sources

DOE Prototype
Building

CoStar Building Type

Location (county)

Dependencies

X
X

52

HVAC controlsa

X

X

X

HVAC
efficienciesa

X

X

X

italics = archetype parameters, which are building characteristics whose values influence the conditional probability
tables of other building characteristics
a For energy simulations, values of these parameters are determined using EnergyPlus/OpenStudio defaults basedon the dependencies shown(i.e., no probability tables are associated with these characteristics).
b The number of counties is dependent on the analysis area.

c Rotation is defined as 8 orientation bins offset by 45 degrees, with a uniform probability distribution.
d Aspect is defined as 6 bins between about 0.5 and 6.5. Each shape uses only some of the bins.
e We infer HVAC system type based on the main heating and cooling types recorded in (EIA 2016a), using lookup

logic from (Griffith et al. 2008).

ComStock, along with ResStock, utilizes actual meteorological year (AMY) weather files
derived from the NSRDB as an input into the EnergyPlus simulation engine (Appendix H).
However, ComStock uses individual weather files for each county in the CONUS. Each weather
file location was chosen to be the closest NSRDB grid cell to the LandScan grid cell with thehighest daytime population in the county (ORNL 2011).
2.2.2.2 Methodology
The conditional probability tables created from ground-truth data sets are stored in tab-separatedvalues file format, and the Parametric Analysis Tool is used to sample these tables hierarchically.

33
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We apply the same Latin hypercube stratified sampling methodology as ResStock (Wilson et al.
2016), selecting values for building characteristics based on the relative probabilities (i.e.,frequencies) of those values, as described in CBECS 2012 and CoStar data. The resulting setsof building characteristics are translated into inputs for energy models in OpenStudio andEnergyPlus format using the Parametric Analysis Tool and OpenStudio measures (Roth,Goldwasser, and Parker 2016). This process is completed for 350,000 building simulations on
internal server systems and cloud-computing infrastructure. Weighting factors are applied toscale results from the simulated 350,000 buildings to the total number of commercial buildingsin the CONUS as estimated by CBECS 2012 (EIA 2016a).
2.2.2.3 Output Data
ComStock output data are similar to those of ResStock. The reported end uses are based onstandard EnergyPlus reportable meters:•

Fans: electricity (kWh)•Pumps: electricity (kWh)•Space heating: electricity (kWh)•Space heating: gas (kBtu)•

Space cooling: electricity (kWh)•Interior lights: electricity (kWh)•Exterior lights: electricity (kWh)•Water systems: electricity (kWh)•Water systems: gas (kBtu)•

Interior equipment: electricity (kWh)•Interior equipment: gas (kBtu)•

Space cooling: district cooling (kBtu)•

Space heating: district heating (kBtu)•

Heat rejection: electricity (kWh)When space heating or cooling is provided by district systems, the quantity of energy reported isthe amount of thermal heating or cooling energy delivered to the building. To capture the load
this represents in terms of electricity and natural gas, we assume conversion factors of 0.58 kWhelectricity/ton cooling-hour for district cooling and 1.25 kBtu natural gas/kBtu heating for
district heating (Xcel Energy 2016).
Based on the reported metadata of each simulation, the results are categorized on a county-bycounty basis. The data are reported by commercial subsector and end use by summing across all
applicable data points within each county to achieve aggregated load timeseries. These values,
along with a scaling factor of 9.034, which is a result of simulating approximately one seventh

34
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of all commercial buildings in each county and a simulation failure rate of approximately 15%, ispersisted and transferred to dsgrid. 32
2.2.2.4 Calibration
The modeling techniques used in the creation of the ComStock building energy models are basedon a significant body of peer-reviewed work completed by NREL, the Pacific Northwest
National Laboratory, and Lawrence Berkeley National Laboratory over the last decade (Deru etal. 2011). The inputs have been reviewed by numerous experts, including ASHRAE committee
members who design these buildings professionally. These models are under a constant state of
update and review through the OpenStudio Standards Gem, an open-source project whose
continual testing and development is supported by several ongoing projects.
As ComStock is still in early stages of development, additional calibration steps are in progress.
Two primary categories of error are currently being examined in the ComStock model. The firstclass of error stems from the uncertainty regarding the number of commercial buildings in the
United States, the square footage of said buildings, and their primary use. The second class isdue to uncertainty in the modeling assumptions being applied and potential bugs in theirimplementation. All work in the immediate future is focused on finding and addressing theseerror sources to (1) improve our estimate of the total stock being modeled in terms of number
and size of buildings by type and (2) improve our estimates of the energy use of these buildings
at different timescales (e.g., annual, hourly, and subhourly).
Data sources (e.g., CBECS and CoStar) disagree on such basic data as the number, size and typeof commercial buildings. Some of these differences arise from different definitions of what
constitutes a commercial building. Determining the validity of these data sets relative to one
another, however, requires a set of ground-truth observations. The ComStock team is proceedingby obtaining such observations from various public and private utility data sets. Doing so allows
for verification of the number of buildings of various rate classes, albeit for limited sets ofgeographies. Additional data sets for building types that are not bought and sold frequently(e.g., federal buildings and schools) are also being incorporated to further supplement CBECS
and CoStar.
The modeling assumptions built into the OpenStudio Standards Gem reflect the best information
available from existing sources. Many of the inputs are taken from building energy performancecodes such as ANSI-ASHRAE 90.1, which governs what properties buildings must have basedon their age, type, and location to minimally comply with energy efficiency codes. Other inputs
are taken from the ASHRAE Advanced Energy Design Guides, which are a series ofpublications written by experts on designing particular building types. The approach to modelingmany of the complex building controls and equipment was taken from the DOE prototype
buildings, which have been developed by NREL, the Pacific Northwest National Laboratory, andLawrence Berkeley National Laboratory over the last decade (Deru et al. 2011; Goel et al. 2014).
Additionally, because of the sheer number of inputs required for each building energy model,
Factoring the simulation failure rate into the overall scaling factor represents an implicit assumption thatsimulation failures are evenly distributed across geographies, building types, etc. The authors do not expect thatassumption to be fully correct, but did also not find any clear, easily correctable trends in the specific simulations
that failed. This is an area we will look at closely and correct in future work.
32

35
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some inputs and modeling assumptions were developed using professional engineering expertisedeveloped by the ComStock team over many years of modeling buildings for a diverse array of
research projects and field studies.
2.2.2.5 Subsector Gap Model
The DOE “reference buildings” and “prototype buildings” represent 17 commercial building
types that are then replicated across climate zones and vintage to create hundreds to thousands ofarchetypes (Deru et al. 2011; Goel et al. 2014). 33 These models 34 serve as a baseline standard
from which each simulated building is articulated in EnergyPlus. The building types documented
in the CBECS 2012 and CoStar data sets must therefore be mapped to these DOE prototype
buildings for modeling purposes. To avoid producing inaccurate energy models, building types
that did not have a reasonable prototype equivalent were excluded from ComStock, and they are
instead included in the gap model. See Appendix B for the full mapping from CBECS and
CoStar building types to DOE prototype buildings, along with associated building counts used
for this analysis.
The CBECS building types included in the commercial building gap model are listed along with
their annual energy use in Table 6. These building types are those that are not mappable to the
DOE prototype buildings but are mappable to CoStar building types (Table C-3 and surroundingtext). This latter mapping is then used to downscale the electricity use reported in CBECS tocounties based on the CoStar building counts. The proxy timeseries for these gaps are theaggregated ComStock timeseries, summed over subsectors and end use. If a county-level profile
is available, it is used; if not, a state-level profile is applied. Unlike with the residential gap
model, we do not expect the end-use breakdowns to hold when applied to these various building
types 35 and so do not supply them.

The DOE “reference buildings” span 16 building types: Large Office, Medium Office, and Small Office;Warehouse; Stand-alone Retail and Strip Mall; Primary School and Secondary School; Supermarket; Quick Service
Restaurant and Full Service Restaurant; Hospital and Outpatient Health Care; Small Hotel, Large Hotel; and Midrise
Apartment. The DOE “prototype buildings” do not include a Supermarket model, but they add a High-Rise
Apartment. Thus, the two sets of archetypical building models together represent 17 commercial building types.
34
ComStock builds off of the OpenStudio Standards gem, which is a programmatic implementation of the DOE
prototype buildings. At the time of this writing, 16 building types: all of those listed above except for Supermarket;
are available. Because of deficiencies in the CoStar data, we are also not able to distinguish between Mid-Rise andHigh-Rise Apartments, nor between Primary and Secondary Schools (school buildings are generally underrepresented in CoStar) at this time. As such, this version of dsgrid provides results for 14 commercial building subsectors: Large Office, Medium Office, Small Office, Warehouse, Stand-alone Retail, Strip Mall, Primary School,
Quick Service Restaurant, Full Service Restaurant, Hospital, Outpatient Health Care, Small Hotel, Large Hotel, andMidrise Apartment. Future planned work to add Supermarkets to the prototype buildings, and to supplement CoStar
with data sources that better represent public-sector building counts, should remedy these shortcomings.
35
Commercial buildings are much more heterogenous than residential buildings, both in design and use. For
example, the end use breakdowns between grocery stores, vehicle repair shops, and libraries look very different;
and, the temporal distribution of load will be very different between offices, libraries, and religious buildings. The
inability to match a CBECS or CoStar building type to a prototype building essentially signals that we do not yet
have an energy model that properly captures one or both of these aspects.
33

36This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table 6. Commercial Sector Subsectoral Gaps: Size and Proxy Timeseries Description

Subsector

Est. Portionof CONUS
Commercial
Electricity
Use (%)

Proxy Timeseries

Description

Geographic
Resolution

End-UseResolution

Grocery store/food market

2.7

ComStock aggregation

CountyNone

Recreation

2.0

ComStock aggregation

CountyNone

Religious worship

1.9

ComStock aggregation

CountyNoneEntertainment/culture

1.6

ComStock aggregation

CountyNone

Laboratory

1.5

ComStock aggregation

CountyNone

Convenience store

1.2

ComStock aggregation

CountyNone

Vehicle service/repair shop

1.2

ComStock aggregation

CountyNone

Convenience store withgas station

1.0

ComStock aggregation

CountyNone

Library

0.9

ComStock aggregation

CountyNone

Vehicle storage/
maintenance

0.7

ComStock aggregation

CountyNone

Other public order andsafety0.6

ComStock aggregation

CountyNone

Vehicle dealership/
showroom

0.6

ComStock aggregation

CountyNone

Fire station/police station

0.5

ComStock aggregation

CountyNone

2.2.3 Industrial Sector
The Industrial Geospatial Analysis Tool for Energy Evaluation (IGATE-E) is a model thatutilizes multiple data sources and statistical approaches to estimate the energy consumption of
manufacturing plants across the United States. Originally developed by Oak Ridge National
Laboratory in 2012, the tool has been used to investigate the potential for demand response and
CHP at the plant levels (Alkadi et al. 2013; Bhandari et al. 2018).
IGATE-E can be differentiated from the other sector models used by dsgrid in that it does not
attempt direct simulation of loads, because of the highly complex and specialized nature ofmanufacturing processes. For example, process heating, which represents one of the largest
opportunities for electrification within the manufacturing subsector, is comprised of more than
10 types of heating operations 36 and 20 distinct system types 37 used to carry out these operations(LBNL et al. 2015). Furthermore, the applicability of each of these operational categories and
36
LBNL et al. (2015) provides basic descriptions of agglomeration and sintering, calcining, curing, drying, fluid
heating, forming, high-temperature heating and melting, low-temperature heating and melting, heat treating,incineration/thermal oxidation, metals reheating, smelting, and other heating processes.
37
LBNL et al. (2015) categorizes process heating systems as fuel-based, electric-based, steam-based, or other;where other is comprised of systems such as heat recovery, heat exchange, and fluid heating. That report lists
14 kinds of fuel-based furnaces and 11 electricity-based process heating technologies.

37
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systems varies greatly from one manufacturing industry to another. Because of the impracticalityof modeling so many different use cases, IGATE-E instead compiles data from multiple sources
and applies statistical techniques to estimate energy consumption down to the plant and end-use
levels. Because IGATE-E only models manufacturing, the additional industrial subsectors ofagriculture, mining, and construction comprise the industrial gap model.
2.2.3.1 Input Data Sets
Within IGATE-E, several data sets are used to estimate energy consumption for the
manufacturing subsector. The primary data sets are the DOE Industrial Assessment Centers(IAC) Database and the Manufacturers’ News, Inc. (MNI) EZ Select database. The IAC
Database is a public data source that contains information on more than 18,000 plant-levelassessments (DOE 2017a). The assessments date to 1981 and primarily focus on small- andmedium-sized plants. 38 Although the IAC Database contains a large amount of data, its
relevancy may be limited in industries where fewer recent assessments have been completed andwhere large manufacturing plants play a major role. Furthermore, because plants must choose toundergo an assessment, a self-selection bias may be present, with a recent analysis of IAC
participants indicating participants were less energy efficient than their peers (Dalzell, Boyd, andReiter 2017). Though a detailed evaluation of the statistical validity of the IAC Database was notconducted, Figure 8 summarizes how many assessments are available by both industry andassessment year. For its use in this analysis, IGATE-E ignores assessments that do not provide
information on a plant’s NAICS code. As a result of this requirement, IGATE-E only uses
assessment data from 2002 onward, limiting problems that could have arisen from using
older data.

Figure 8. Number of IAC assessments by industry and year (as of March 2018)
For assessments occurring before 2002, results are available by SIC code only.
NAICS = North American Industry Classification System

Eligibility requirements to qualify for an IAC Assessment include energy bills between $100,000-/yr and
2,500,000/yr.
3838This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Additional data sources have been explored to supplement this database; however, theproprietary/competitive nature of plant-level information has made it difficult to find additional
data beyond what is currently available from the IAC Database.
The MNI database is a commercial data set that provides industry, location, and employment
information for approximately 294,000 manufacturing sites across the United States, which
provides approximately complete coverage of manufacturing establishments, see Footnote 44.
Other sources were considered for this purpose, such as the Dun & Bradstreet Hoovers database,but the MNI was found to be the most cost-effective based on the needs of the project. Together,
the MNI and IAC data sets are used to develop plant-level energy and demand estimates.
To refine IGATE-E’s initial energy consumption estimates, data from the 2014 MECS are used.
Finally, end-use level consumption information by industry (i.e., three-digit NAICS) from theMECS, along with data from EPRI’s Load Shape Library, 39 is used to construct disaggregated8,760 load shapes. Table 7 summarizes the manufacturing plant characteristics, modelingmethods, and data sources used by IGATE-E.
Table 7. Manufacturing Plant Characteristics, Modeling Methods,
and Data Sources used by IGATE-E

Plant Level

Location (State/County/Zip Code)X

Energy Consumption (kWh or MMBtu/yr)

X

Sectoral LevelX

EPRI Load ShapeLib

MNI EZ SelectD t b2014 EIA MECS
XXElectricity Demand (kW/month)

Industry Level

IAC Database

X

Industry Code (NAICS/SIC)

Number of Employees

Data Sources

Load Factor
A l i
Load Shape Analysis

Energy Analysis

Characteristics

Energy Analysis

Resolution

Regression Analysis

Methods

X

X

X

X

X

X

X

X

Industry Code (NAICS/SIC)

X

Energy Consumption (kWh or MMBtu/yr)

X

X

X

X
X
X

End-Use Energy Consumption (kWh/yr)

X

Load Shapes by End-Use

X

X
X

39
The EPRI Load Shape Library provides representative industrial load shapes by end-use and region. Daily load
shapes are provided for various scenarios (i.e., weekday versus weekend and peak season vs. off-peak season). Loadshapes are derived from simulations using the EPRI NESSIE (National Electric System Simulation Integrated
Evaluator) model platform. The inputs to NESSIE are derived from data estimated by the EIA’s National Energy
Modeling System (NEMS) as well as from data collected by EPRI through its laboratory testing and research.

39
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2.2.3.2 Methodology
To estimate electricity consumption within the manufacturing subsector, IGATE-E performs
regression analyses of energy consumption versus number of employees using data from the IAC
Database. Regression results are developed for individual industries based on their North
American Industry Classification System (NAICS) 40 code; this system is used to classify
business establishments according to their primary economic activity, specifically the type of
product being produced. Within the model, a linear regression analysis is conducted by industry
for every three- and four-digit NAICS code. 41
Working within this framework, the basic premise of IGATE-E is that manufacturing facilities
producing similar products (as categorized by their NAICS code) will utilize similar processes
that ultimately have similar energy intensities (i.e., energy usage per product produced).
Currently, number of employees is used as a proxy for the product being produced. 42 Using theseindustry-specific regression results, aggregate energy usage is estimated for each plant using
employment information from the MNI database. Finally, usage estimates are adjusted byindustry (three-digit NAICS) and census region to better align with the MECS.
Hourly annual load shapes are created by combining industry-level load factor estimates with
diurnal load shapes provided by sector and end use from the EPRI Load Shape Library. Loadfactors are estimated from the IAC Database data by conducting a regression analysis of peakelectricity demand versus annual electricity use and then estimating an average load factor foreach industry based on the slope of these regressions. Plant-level peak electricity demand falls
out by combining load factor with annual energy consumption estimates. The EPRI Load ShapeLibrary provides daily load shapes by sector and end use for various scenarios (e.g., weekday
versus weekend). To disaggregate energy consumption at the end-use level, data from the 2014
MECS (by three-digit NAICS) is applied. Next, categories included in the EPRI Load ShapeLibrary are matched to the most appropriate MECS end-use category (Table 8). Finally,
disaggregated load shapes are constructed for each industry based on end-use consumption
estimates from the 2014 MECS and load shapes from the EPRI Load Shape Library. The
resulting load shapes are either “stretched” or “flattened” on an industry-by-industry basis tomatch the load factor estimates derived previously. Within IGATE-E, load shapes are applied at
the individual plant-level based on a plant’s peak electricity demand.

For more information, see https://www.census.gov/eos/www/naics/.
Within IGATE-E, a minimum of five IAC assessments per regression is enforced. While most four-digit
industries have enough data to meet this requirement, those that do not utilize three-digit regressions.
42
IGATE-E originally utilized annual sales data for this purpose; however, inconsistencies in quality and thesporadic availability of this data limited its value. Although nominally annual sales should correlate better to energy
usage than number of employees, the latter data field is more consistently provided in IGATE-E’s data sets, and it
has thus proven to be a better proxy in practice.
40
41

40
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Table 8. End-Use and Load Shape Category Mapping
2014 MECS
End-Use Category

EPRI Load ShapeLibrary Category

Conventional Boiler Use

OtherProcess Heating

Process Heating

Process Cooling and Refrigeration

Other

Machine Drive

Machine Drives

Electro-Chemical Processes

Other

Other Process Use

Other

Facility HVAC

HVAC

Facility Lighting

Lighting

Other Facility Support

Other

Onsite Transportation

Other

Other Nonprocess Use

Other

End Use Not Reported

Other

2.2.3.3 Output Data
For use in dsgrid, plant-level peak demand estimates are compiled by county and NAICS code.
These peak demand values are then mapped to the corresponding normalized load shape. Loadshapes are available on a per-four-digit NAICS code and time zone basis (conforming to thedsgrid convention of 2012 as experienced in EST, with end-of-hour data points, and accounting
both for time zones and for varying daylight saving time policies). Within the load shapes,
electricity consumption detail is provided for the following end-use categories: conventional
boiler use, process heating, process cooling and refrigeration, machine drives, electrochemical
processes, other process use, facility HVAC, facility lighting, other facility support, onsite
transportation, and other nonprocess use. These categories match those reported in the MECS. 43
2.2.3.4 Calibration
Several differences emerge when IGATE-E’s estimates are compared to the 2014 MECS.
Industries where consumption is significantly underestimated include:•322: Paper•

324: Petroleum and Coal Products•

325: Chemicals•

331: Primary Metals.

In reviewing the MECS, these industries were found to have the highest energy intensities,
suggesting the lack of regression data for large manufacturing plants may be limiting IGATE-E’s
In addition to electricity consumption, IGATE-E also estimates annual natural gas consumption at the individual
plant-level. Currently, however, no effort has been made to disaggregate natural gas usage by end-use category.

43

41
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accuracy in these cases. For industries where consumption is significantly overestimated,discrepancies in the number of establishments and employees considered by MECS compared to
IGATE-E may be the primary issue. 44
While additional research is being conducted to understand these differences, IGATE-E’s initial
estimates are adjusted by industry and census region to match the MECS. This is accomplishedby scaling individual plant estimates so that aggregate consumption estimates match those fromthe 2014 MECS. The primary drawback to this is that errors in the MECS are ultimately
reproduced by IGATE-E. Future efforts will focus on further developing IGATE-E’s
optimization approach to avoid “overadjusting” initial estimates to match the MECS.
2.2.3.5 Subsector Gap Model
Within the industrial sector, IGATE-E’s methodology has specifically been developed for the
manufacturing subsector. Subsectors not covered by IGATE-E include agriculture, forestry,
fishing and hunting; mining, quarrying, and oil and gas extraction; and construction. In dsgrid,these non-manufacturing subsectors are represented in the industrial gap model using annual
electricity estimates from the AEO and generalized load shapes from the EPRI Load ShapeLibrary. To develop county level results for these subsectors, AEO national estimates are firstdisaggregated to the state level based on employment data from the U.S. Census Bureau’s
Statistics of U.S. Businesses and then at the county level based on number of establishments datafrom the U.S. Census Bureau’s County Business Patterns (CBP).
Table 9. Industrial Sector Subsectoral Gaps: Size and Proxy Timeseries Description
Proxy Timeseries

Subsector

Est. Portion of
CONUS
Industrial
Electricity
Description
Use (%)

Geographic
Resolution

End-UseResolution

Mining, quarrying, and oil
and gas extraction

8.8

IGATE-E genericindustrial load shape

CountyNoneConstruction

6.4

IGATE-E genericindustrial load shape

CountyNone

Agriculture, forestry, fishingand hunting

3.7

IGATE-E genericindustrial load shape

CountyNone

2.2.4 Transportation Sector
For dsgrid, we are primarily interested in electrification of road transportation, namely electricity
use for passenger and commercial plug-in hybrid electric vehicles and battery electric vehicles.
To model this electricity use, we rely on three NREL models, as summarized in Figure 9:

For example, while the MNI database and the U.S. Census 2015 Statistics of U.S. Businesses(https://www.census.gov/programs-surveys/susb.html) largely agree on the number of manufacturing
establishments, reporting 294,427 and 292,825 respectively; the 2014 MECS estimates 175,107 establishments.
44

42This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

•

Automotive Deployment Options Projection Tool (ADOPT): used to inform vehicle
adoption and vehicle attributes (e.g., range and fuel economy)•

Scenario Evaluation and Regional Analysis (SERA): used to provide spatiallydistribute regional vehicle adoption•

Electric Vehicle Infrastructure Projection Tool (EVI-Pro): used to generate
hourly charging profiles based on travel data and charging preference assumptions(e.g., residential charging as opposed to reliance on public charging).

Core bottom-up transportation modeling in dsgrid focuses on electrification of on-roadtransportation via PEVs (including plug-in hybrids), for both light-duty and medium- and heavyduty vehicles. All other forms of transportation (e.g., rail, air, marine, and off-road vehicles) areconsidered in the gap model. Moreover, we do not consider alternative electrification strategies
such as battery swapping or dynamic charging technologies (e.g., embedded roadway or catenary
charging) that may be especially appropriate for heavy-duty on-road vehicles (Navidi, Cao, andKrein 2016; Cordoba Ledesma 2015). Note that although off-road vehicles are generally
considered a transportation gap in dsgrid, energy consumed by off-road vehicles used inconstruction, agriculture, or mining would be categorized as an industrial, rather than atransportation gap, in line with energy consumption statistics (EIA 2017c).

••

Overallelectrification
ambition
LDV/MDV/HDV
role

VehicleAdoption•

Scenario
Definition•

Based on cost,
performance,and projected
improvement

Real-World
Hourly Power
ProfilesBased onspatialdistribution ofearly adopters

Regionalizationof Vehicle
Deployment•

Based on travel
surveys, vehicle
characteristics,
and charging
assumptions

Figure 9. Transportation modeling scheme for the road subsector
LDV = light-duty vehicle, MDV = medium-duty vehicle, HDV = heavy-duty vehicle

The electricity consumption in road transportation in 2012, however, was very limited, withroughly 70,000 plug-in electric passenger vehicles on the road concentrated in a few urban areas(IHS Markit 2017). This number represents about 0.4 TWh of electricity, or less than 0.05% of
U.S. electricity use (EIA 2015a). Therefore, it is not included in the 2012 historical dsgrid
snapshot that is the focus of this documentation, but we do here document the methodology thatwill be used to model PEV charging load profiles and flexibility for future scenario snapshotsthat include higher PEV market shares. We also describe our transportation gap model, which
consists of a first-order spatial and temporal disaggregation of transit rail electricity use, which
accounted for approximately 6.6 TWh in 2012 (EIA 2015a).

43
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In the remainder of this section, we document how PEV electricity use will be estimated in the
EFS future load snapshots using SERA and EVI-Pro, given (1) an exogenously specified fleet ofelectric vehicles (informed by ADOPT runs for passenger PEVs) and (2) how rail transit
electricity use has been disaggregated in the gap model. Given electrification scenarios designed
as part of the EFS project, which define overall (national) adoption of electrified technologies inthe transportation sector (Mai et al. 2018), SERA will be used to disaggregate to the county level
and EVI-Pro will be used to develop hourly charging profiles. Charging flexibility andwillingness to delay charging will be assessed in a scenario framework based on bounding
conditions and expert judgement. The potential to use that flexibility to provide grid services andparticipate in demand response will not be considered here, but it will be assessed using
operational grid models in future work.
2.2.4.1 Input Data Sets
To describe electricity use in the on-road transportation sector, we rely on vehicle registration
data from IHS Automotive, vehicle attributes from the ADOPT model, consumer attitude andpreference for alternative fuel vehicles included in the SERA model, and numerous travel
surveys and charging behavior assumptions included in the EVI-Pro model. Table 10
summarizes the dependencies and data sources used to model plug-in vehicle electricity
use in dsgrid.
Table 10. Vehicle Characteristics, Dependencies, and Data Sources Used in thedsgrid Transportation Modeling Process

PEV characteristics

x

x

x

x

x

x

x

x

x

x

x

x

x
x

x

PEV use patterns

IHS Automotive

EFS assumptions

EIA AEO

x

x

x

x
x
x

x

x

x

x

x
x

Charging profiles (hourly)

EVI-Pro (sources therein)

Othersx

SERA (sources therein)

Commercial PEVs

Spatial disaggregation

ADOPT (sources therein)

x

x

x

x

Passenger PEVs

Passenger Vehicles Overall PEV adoption

Charging behavior

x

Data Sources

Infrastructure availability

Transportation electricity use

Technology development

Subsectors

Consumer preference

Characteristics

EFS scope

Category

Overall electrification

Dependencies

x
x

x

x

x

x

44
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Dependencies
Commercial
VehiclesOverall PEV adoptionxx

Data Sourcesx

Spatial disaggregationx
x

Electrification strategy

xVehicle attributesxxx

Charging profiles (hourly)xxx

Vehicle use patterns

x

x

x

x

x

x

2.2.4.2 Methodology
Electricity use for passenger light-duty vehicles is modeled using a suite of mature tools
developed by an NREL transportation team. These tools project vehicle adoption in the lightduty sector and alternative fuel infrastructure build-outs, and they model the energy use ofindividual passenger and commercial vehicles, including charging profiles for PEVs. The version
of dsgrid documented here focuses on electrification of road transport, including passenger
vehicles as well as medium- and heavy-duty commercial vehicles. Three different models are
combined to arrive at hourly, county-level electric vehicle charging profiles. Detailedtransportation models are used to project electrification of road transportation (which makes up
approximately 79% of the approximately 26 quads of 2012 transportation energy consumption
and is the most likely sector to be impacted by electrification). Other subsectors (rail, marine
shipping, and aviation) are not be considered in detail.
The ADOPT tool will be used to inform passenger vehicle adoption scenarios over time basedon characteristics of the existing vehicle fleet, technical and cost targets, and policy assumptions(Brooker et al. 2015). ADOPT will also project attributes and characteristics of future vehicles(e.g., battery capacity and vehicle range, and fuel economy) ADOPT provides yearly estimatesof sales and vehicle stock at the county level that are further geospatially resolved using the
SERA model (Bush et al. 2017). SERA has a highly geographically resolved understanding oftransportation demands— down to the 0.5-km scale—and it will properly regionalize the
ADOPT projections as needed and provide regional estimates of the resulting annual electricity
demand. SERA will also be used to estimate potential for electrification for medium and heavyduty commercial vehicles based on scenarios informed by relevant literature and current truck
traffic volumes resolved in time and space. Medium-duty battery electric vehicles and buses will
be modeled based on relevant literature in a scenario approach (i.e., educated assumptions about
electrification of the existing fleet). In the heavy-duty road sector—although battery-powered
trucks are technically conceivable—the range and the fuel energy density (both in terms ofweight and volume) requirements make the deployment of PEVs harder (even though some
private companies have been working to develop such products). Therefore, truck electrification
will be modeled based on instantaneous charging, either wireless charging or charging via
electrified road corridors (catenary charging). These charging profiles will be estimated for key
corridors using heavy-duty traffic information and spatially disaggregated using the SERA
model.

45
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Finally, given a projected adoption of passenger and medium-duty PEVs for a given year, EVIPro will be used to estimate hourly charging profiles (Wood et al. 2017). EVI-Pro considers
different recharging infrastructure scenarios, informed by the SERA model, including
preferential use of residential, workplace, and public charging stations. EVI-Pro uses real-world
travel data to simulate spatially and temporally resolved demand for PEV charging at homes,
workplaces, and public destinations. It anticipates consumer charging behavior while capturing
variations with respect to housing type (single versus multiunit dwellings), travel period(weekdays versus weekends), and regional differences in travel behavior and vehicle adoption.
Figure 10 illustrates the main modeling steps in EVI-Pro.

Figure 10. EVI-Pro model structure
Source: Wood et al. (2017)
eVMT = electric vehicle miles traveled

A fundamental assumption in EVI-Pro is that consumers prefer charging scenarios that enable
them to complete all their current travels (which are driven in gasoline vehicles) and to maximize
the miles driven on electricity (for plug-in hybrid electric vehicles). To define which charging
scenarios consumers will elect, individual travel days from available travel surveys are simulated
in the model. Each travel day is simulated multiple times for each potential combination of
charging behavior (e.g., L1-Home, L2-Home, and L1-Home plus L1-Work) and the lowest-cost
option if then selected, considering different levels of consumer preference for alternative
charging solutions.
Past works using ADOPT, SERA, and EVI-Pro focused primarily on light-duty vehicles. For the
future load snapshots, the team plans to construct a fairly detailed model of medium- and heavyduty electric vehicles, based on the EFS focus on on-road transport electrification. To estimate
46
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the adoption of such vehicles the team will leverage internal expertise and coordinate with thescenario development group to produce realistic projections.
2.2.4.3 Output Data
ADOPT, SERA, and EVI-Pro are mature models that have been used extensively to answer avariety of questions related to transportation sector transformation and potential implications interms of refueling infrastructure deployment and impact on the electric grid. The EFS, however,will push the boundaries of the three models by integrating them to generate spatially andtemporally highly-resolved profiles for a set of different electrification scenarios. For the futurescenario load snapshots, the models will be combined to produce hourly baseline charging andcharging flexibility metrics (e.g., charging “schedulability” profiles) by subsector and scenario(specified by a set of charging preferences) for each county in the CONUS.
2.2.4.4 Calibration
SERA and EVI-Pro, which will be used to project future electricity demand for roadtransportation, have been calibrated using current vehicle use and statistics based on available
regional and National travel surveys (Bush et al. 2017; Wood et al. 2017).
2.2.4.5 Subsector Gap Modeldsgrid models on-road transportation at a high level of detail. For the purposes of describing
dsgrid, all other transportation subsectors are “gaps.” For this version of dsgrid, we focus on
the amount of electricity used for transportation in 2012, a total of about 7.0 TWh. Most of
the electricity used in transportation in 2012 was used by trains: 6.6 TWh partitioned amongintercity rail, transit rail, and commuter rail, per EIA (2015a). This energy use is disaggregated in
the transportation gap model to produce hourly profiles using a first-order disaggregation method
based on NTD data (FTA 2017). In particular, total electricity consumption for rail isdisaggregated spatially based on annual electric energy expenditure for propulsion (in kWh),as reported by NTD for 67 companies covering 45 U.S. urban areas (FTA 2017). To resolve
this demand hourly, because no detailed modeling is available, we leverage data on hours ofoperation for different days of the week and number of rail cars operating in each day, which arealso available in the NTD. Table 11 summarizes this gap model. Additional details are provided
in Appendix D.
Table 11. Transportation Sector Subsectoral Gaps: Size and Proxy Timeseries Description
Proxy Timeseries

Subsector

Passenger rail

Est. National
Electricity
Use (TWh)
Description

6.6

Constructed based onhours of operation andnumber of rail cars
operating by day type

Geographic
Resolution

End-UseResolution

State

None

2.2.5 Summary
The purpose of dsgrid is to estimate potential future load shapes, especially as they might be
impacted by energy efficiency, electrification, and demand response (i.e., set-point and47
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operational modifications made in support of grid operations). Given this, an initial gap analysis
of our planned model was completed up front to assess how much electricity use—and how
much of all potentially electrifiable site energy use—is covered by the bottom-up sector models,as these proportions give a first indication of how well we should expect to be able to model
major shifts in future electricity load. We have also revisited the gap analysis to accuratelyreflect what in the end is covered in our detailed and gap sector models, as well as what energy
use remains unmodeled by dsgrid.
The analysis uses subsectoral data available in 2009 RECS, 2012 CBECS, 2014 MECS and the2012 historical data available in the AEO 2015 (EIA 2013d, 2016a, 2017a, 2015a). 45 We
surveyed the sector modeling teams to determine which subsectors are fully described, which areincluded in our sectoral gap models, and which are not included in either of these categories. The
proportion of U.S. electricity use and total site energy use modeled in detail, coarsely, or not at
all was then estimated by tagging subsectors in the national-level data sets. Details about themodel coverage analysis methods and results are available in Appendix E.
For the baseline dsgrid snapshot described here, perhaps the most important metrics are those
associated with 2012 electricity use. As shown in Figure 11, dsgrid models about 80% of current
U.S. electricity use at full subsector and end-use resolution. Looking across all electricity use,
one can see the largest gaps are in commercial and residential buildings. Industrial energy usethat is not covered by IGATE-E includes mining, construction, and agriculture. Transportationelectricity use is currently very small and consists mostly of passenger rail, which is not modeled
in detail by dsgrid but is assigned an hourly timeseries of electricity use by state. The actual
sectoral gaps are listed in detail above, in Table 3, Table 6, Table 9, and Table 11, and in thesurrounding text.

Figure 11. dsgrid models about 80% of 2012 U.S. electricity use in detail.
It is generally the case that coverage in our sector models is mostly determined by sub-sector rather than bygeography or end use.
45

48
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Looking forward to questions of end-use electrification and operational flexibility—andrecognizing that our sector models model all energy use, not just electricity—we extend ouranalysis of model coverage to compare total energy use by each fully modeled subsector against
the total energy use of the sector. This comparison is done on a site energy basis, based on
dsgrid’s focus on loads as they are experienced from the utility customer perspective. The
results of that breakdown are shown in Figure 12.

Figure 12. dsgrid models about 76% of 2012 U.S. site energy use in detail.

When examining site energy use, the large roles of transportation and industry in our energy
economy quickly become clear, as does the large potential for electrification. With a coverage by
this metric of 76%, dsgrid appears to be well-positioned to tackle questions about what potential
roles end-use electrification could play in our energy future. While electricity made up only 23%
of site energy use in 2012, 76% of the non-electric remainder was used for nominallyelectrifiable end uses, including electrifiable transport (comprising 46% of non-electricenergy consumption) and space and water heating (17%) (Appendix E).
The significant gaps in coverage from a site energy perspective are approximately the same as
those from the electricity-only perspective for commercial and residential buildings, as well as
industry. For transport, fuel use other than electricity opens additional subsectors, such that themost significant gap in our transportation modeling when all fuels are considered is no longer
rail, as it was when we just looked at electricity. Considering all fuels, the biggest transportation
subsectors we do not model in detail are air, shipping and freight, natural gas pipelines, andmilitary transport.

49
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The electricity and energy coverages provided by the core sector models, plus a description of
their geographic, temporal, and sectoral extents and resolution, are summarized in Table 12.
Each sector model was further developed for this project, compared to where they were at thestart in order to support the needs of power system modelers in general, and the EFS specifically.
Of the four sectors, residential and transportation started at fairly mature levels of model
development. The residential team thus focused on validating the ResStock hourly profiles by
end use, and geographically downscaling the results to counties by leveraging the ACS and othergeographic information system data layers. The transportation team focused on developing
supplemental models of rail electricity use, and electrification of medium- and heavy-duty
vehicles, the latter in anticipation of later stages of the EFS. Before this project, IGATE-E had
been used to estimate demand response and CHP potential for the industrial manufacturingsector. Work for the EFS included further validation of annual energy use estimates, andimproved methods for constructing hourly timeseries of electricity load. ComStock did not exist
before this work, but it was possible to build the model up in a timely manner by leveraging the
DOE prototype buildings and the methodology pioneered by the ResStock team.

50
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Table 12. Geographic, Temporal, and Sectoral Extents, Resolutions, and Biggest Gaps for dsgrid’s Constituent Models
Buildings

Industry

Transport

ResStock

ComStock

IGATE-E

SERA

EVI-PRO

ExtentContiguous United States

Contiguous United States

United States

Contiguous UnitedStatesContiguous UnitedStatesResolution

CountyCountyZIP Code

0.5-km gridCounty (flexible)

ExtentAnnualAnnualAnnualDecadal

Daily

Resolution

Subhourly

Subhourly

HourlyAnnualSubhourly

ExtentSingle-family detached

Sixteen DOE commercial
prototype buildings

Manufacturing

Passenger andcommercial vehicle
stocksPEVs

Resolution

6,000 conditional
probability distributions
adjust 80 model inputs.

3,400 conditional
probability distributions
adjust nine model inputs.

86 four-digit NAICS
codes

Flexible based on
inputsResidential,
workplace, andpublic recharging

Subsectoral
Gaps

Single family attached,
2–4 unit apartments,
mobile homes

Grocery store, recreation,
religious worship,entertainment/culture,laboratory, convenience
store, vehicle
service/repair;
Other building types are
not modeled.

Agriculture and forestry,mining and otherextractive industries,
construction

Electricity for transport by rail; other fuel used
for air, natural gas pipelines, military transport,
shipping, rail is not modeled.

Resolution

Fans, pumps, heating,cooling, interior lights,
exterior lights, water
systems, interior
equipment

Fans, pumps, heating,cooling, interior lights,
exterior lights, water
systems, interior
equipment, district
heating, district cooling,heat rejection

Boilers, process heating,process cooling, machine
drives, electrochemical
processes, facility HVAC,
facility lighting, facility
other, non-process uses

2012
Electricity
Coverage

1,310 TWh
83% detailed model,
17% gap model

1,429 TWh
73% detailed model,
16% gap model,
10% unmodeled

1,331 TWh
86% detailed model,
14% gap model

7 TWh
5% detailed model,
95% gap model2012 Site
EnergyCoverage

10 Quads
83% detailed model,
17% gap model

8 Quads
74% detailed model,
17% gap model,
9% unmodeled

15 Quads
67% detailed model,
33% gap model

26 Quads
79.4% detailed model,
0.1% gap model,
20.5% unmodeled

Geographic

Temporal
Sectoral

End-UseEV charging

51
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2.3 Gap and Derived Models

To develop a complete picture of U.S. electricity use, dsgrid includes gap models for subsectorsthat are nominally classified as commercial but are unrelated to commercial buildings, anddistributed generation models. Derived components describing power system losses and model
residuals are constructed by leveraging top-down data sources that describe load from theelectricity sector point-of-view and by comparing them to the other components of dsgrid.
2.3.1 Non-Sectoral Gap Models
Several types of energy use fall outside easy categorization. There are municipal uses of energy,
such as water distribution and treatment, wastewater treatment, and outdoor lighting. There arealso commercial enterprises such as amusement parks and ski resorts that in a sense could be
categorized as “commercial buildings” but defy traditional building energy modeling. The EIA,at least on Form 861, designates all these subsectors as commercial (EIA 2013c). Here we limit
our attention to the three municipal subsectors mentioned above: water supply, wastewater
treatment, and outdoor lighting.
2.3.1.1 Municipal Water Services
Municipalities and other water utilities provide two main services: public water supply andwastewater treatment. Public water supply involves providing, treating, and distributing waterto buildings and other locations such as parks and campgrounds. Energy is required to move thewater, typically through pipelines, and to treat it to bring its quality up to drinking water level.
After use, some water is in turn collected by wastewater utilities, which treat it to make itsuitable for returning to the environment. This is also an energy-intensive process.
Pabi et al. (2013) provide a detailed description of the public water supply and wastewater
treatment processes used in the United States and develop a national-level estimate of electricity
used for those purposes in 2011; they find that these processes were responsible for about 1.8%of all U.S. electricity use. Our gap model uses the Pabi et al. (2013) numbers, first broken down
on a per capita basis and then built back up for our geographic scope in 2012. 46
For public water supply, the decomposition was accomplished by computing an average energy
intensity in kilowatt-hours per million gallons supplied (kWh/MG) as shown in Table 13, andcombining this with the estimate that each public water system user uses 171 gallons per day47(Pabi et al. 2013). Because the total U.S. population in 2011 was about 313 million,48 the percapita use of public water is about 166 gallons per day and thus the annual energy use for public
water supply is estimated to be 125 kWh per year per person for most locations in the CONUS.
For eight counties in Southern California, we apply an additional 7,610 kWh/MG of pumping
energy (Navigant Consulting, Inc. 2006), bringing the annual energy use for those counties up to
586 kWh per year per person. The counties this estimate applies to are Ventura, Los Angeles,
The estimates in Pabi et al. (2013) are for the entire U.S. The scope of this report is the continental U.S. (excludes
territories, Alaska, and Hawaii).
47
This number was originally derived from USGS data on public water withdrawals and EPA data on population
served by public water systems, both reporting for the year 2005.
48
This is the number cited in Pabi et al. (2013). Updated estimates are higher—315.3 million for the states andPuerto Rico in 2011. We use the number cited in the source paper since it is likely more in line with the informationthat was used to estimate the population served data in Table 14.
46

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Orange, Riverside, San Diego, San Bernardino, Imperial, and Inyo, which were selected basedon an examination of the description of California’s hydrologic regions found in California
Department of Water Resources (2009).
Table 13. Public Water Supply Energy Intensity and Population Served
SourceEnergy Intensity (kWh/MG)

Population Served

Surface

1,600

205,181,000

Groundwater

2,100

89,225,000

Desalination

12,0009,416,000

Population-weighted average: 2,069

Total: 303,822,000

Summary of data from Pabi et al. (2013)

The estimate of annual electricity used per capita for wastewater is computed similarly, exceptthat the unit of service in this case is millions of gallons of water treated per day, rather than
population served. Average energy intensity is computed as in Table 14, from which we also findthat 35,845 MG were treated in 2011. After combining that estimate of amount of water treated
per day with the U.S. population in 2011, we estimate that 114 gallons of water are treated per
capita per day, which translates to an average energy use of 96 kWh per year per person.
Table 14. Municipal Wastewater Treatment Energy Intensity and Quantity TreatedTreatment Type

Energy Intensity (kWh/MG)

Water Treated (MG/day)Less thansecondary

750

422

Secondary

2,080

13,142

Greater thansecondary

2,690

16,776

No discharge

2,960

1,815

Pumping reuse
water

1,280

3,500

830

190

Population-weighted average: 2,310

Total: 35,845

PartialSummary of data from Pabi et al. (2013)

To create hourly profiles by county, we multiply the energy per person per year by the U.S.
Census Bureau population-by-county estimates for 2012 (Ruggles et al. 2017); we then use daily
residential water use profiles from AWWARF (1999) (Figure 13a) as a proxy for when energy isneeded for conveyance and treatment. We assume that the energy needed for the public watersupply roughly follows the “total” profile and that the energy needed for wastewater treatment
roughly follows the “indoor” profile, because all the water being used must be supplied, butwater used outdoors does not generally flow back to the wastewater treatment plant. To create
the daily profiles actually used in dsgrid (Figure 13b), we additionally layer on information
pulled from wastewater treatment load profiles reported by Thompson et al. (2008). In Figure 1
in that report, we first see that even though the water use profiles in AWWARF (1999) are for
53
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residences only and the wastewater treatment plant presumably serves establishments other thanhouseholds, the residential water use pattern of one peak in the morning and another in theevening are still apparent in the load profiles for an entire wastewater treatment plant. This does
not cause us to make any adjustments to the profiles, but it does give us some confidence that theresidential water use shapes are a reasonable, if far from ideal, starting point. Second, we noticethat the wastewater treatment plant has a large baseload; that is, its load is typically between
1,500 kW and 2,200 kW. By pulling the minimum and maximum load for each day shown, we
further estimate an average ratio of daily minimum to daily maximum load of 0.75, and we
impose this on our public water supply and the wastewater treatment load shapes in going from
Figure 13a to Figure 13b. Finally, based on comparing the timing of the two peaks in Figure 13a,to the timing of the two peaks in the Thompson et al. (2008) Figure 1 curves, we also impose atime shift of two hours on the wastewater treatment load curve. That is, we assume it takes twohours from when water is used for the wastewater treatment plant to see that water use as anincrease in its electricity usage.

Figure 13. Diurnal profiles for (a) residential water use (AWWARF 1999) and (b) dsgrid municipal
water services gap model

2.3.1.2 Outdoor Lighting
Buccitelli et al. (2017) provide national-level estimates of all lighting energy use in the UnitedStates, first broken down into the gross categories of residential, commercial, industrial, andoutdoor, and then providing significant granularity for types of fixtures and bulb technologies
used per subsector and room-type (residential), per sub-subsector (commercial and industrial)and per application type (outdoor). Because the dsgrid sectoral models already estimate theirown lighting use, the interest here is in the estimates for outdoor lighting energy use. Buccitelli etal. (2017) provide descriptions and energy use estimates for nine outdoor lighting applications:airfields, billboards, commercial and industrial building exteriors, communication towers,
parking, railways, roadways, sports fields, and traffic signals. The total energy use Buccitelli etal. (2017) attribute to these outdoor lighting applications in 2015 is 202 TWh; they estimate that
only three contribute more than 1 TWh to the total, namely parking (103 TWh), roadways (63TWh), and commercial and industrial exterior lighting (33 TWh). Commercial and industrial
exterior lighting is represented in ComStock and IGATE-E. Our outdoor lighting gap model
represents the parking and roadway lighting components based on these and additional data in
Buccitelli et al. (2017) combined with per-capita normalization and solar data that indicate whenthese types of outdoor lights would be expected to turn on and off.
54
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In addition to presenting 2015 estimates, Buccitelli et al. (2017) update earlier (Ashe et al. 2012)
estimates of 2010 lighting energy use based on revised methodology. We therefore normalize
these estimates for 2010 and 2015 by the national population in those years and then interpolate
to obtain 2012 per-capita estimates of electricity use for outdoor lighting for parking androadways (Table 15). Ultimately, electricity consumed for these applications is specified bycounty using the same population by county estimates used in the municipal water services gap
model (Ruggles et al. 2017).
Table 15. Estimates of Absolute and Per-Capita Energy Used for Outdoor Lighting
Energy Estimate Year

UnitsaParking

Roadway

2010

TWh/yr113

66

kWh/person-year

365

213

TWh/yr103

63

kWh/person-year

321

196

kWh/person-year

349

207

2015
2012

a Values in TWh/yr are from Buccitelli et al. (2017). Those estimates are normalized to kWh/person-year with U.S.
population estimates of 309,348,193 for 2010, and 320,896,618 for 2015. We interpolate by year and scale by thefact that 2012 is a leap year to obtain the normalized value for 2012.

Hourly load shapes are determined by inverting county-level, per-unit solar radiation profiles
constructed from NSRDB data initially accessed for the distributed PV model (Section 2.3.3).
To smooth out the hourly profiles, and recognizing that the on and off times of outdoor lighting
are not fully synchronous, we first compute a moving-window average of solar radiation for eachhour in the day (using standard time convention throughout the year), and we then invert the
profile using the function:01
𝜋𝜋𝑓𝑓 = �
�cos � ∙ 𝑥𝑥� + 1�2
𝑡𝑡

𝑥𝑥 > 𝑡𝑡

0 ≤ 𝑥𝑥 ≤ 𝑡𝑡

where f is the outdoor lighting fraction, x is the solar radiation fraction, and t is a threshold value
representing that a certain fraction of solar radiation (compared to the annual maximum) implies
that all outdoor lighting (at least for parking lots and roadways) should be off. For this version of
dsgrid, profiles are generated using a moving window average that for every data point takes themean of the solar radiation for that day and time along with the solar radiation at the same hourof the day for the previous 30 days and the coming 30 days; and we take the threshold on
fractional solar radiation to be 0.05 in the above equation. The resulting transformation is
illustrated in Figure 14.

55
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Figure 14. Sample single-day (a) solar radiation profile and (b) the outdoor lighting profile that
results for the same day from applying the transformation described above
The sample is Washington, D.C. on March 15, 2012.

2.3.2 Distributed Generation Models
Because dsgrid is primarily a bottom-up model of site energy use, distributed generation
estimates must be layered on to estimate the net load that is ultimately served by load-serving
entities/distribution utilities. In the CONUS in 2012, approximately 78,319 MW of CHP andother fuel-based distributed generation capacity and 1,475 MW of DPV generation wereoperating and serving a variety of residential, commercial, and industrial loads. Together,
these two technologies comprise our distributed generation model.
2.3.2.1 Combined Heat and PowerThe DOE Combined Heat and Power (CHP) Installation Database provides information on
CHP plants that are large and small (1 kW to 1,633,000 kW); are of different types (e.g., steam
turbine, fuel cell, reciprocating engine fueled by coal, oil, natural gas, wood, and waste heat);provide different services (e.g., for manufacturing, universities, hospitals, agriculture, andutilities); and operate all over the United States. However, though this data source provides
capacity and location information in addition to the characteristics already described, it does notprovide any indication of typical operation schedules or annual generation quantities (ICF Inc.
and DOE 2016).
In contrast, using both EIA Form 860 and EIA Form 923, it is possible to obtain estimates ofcapacity, generation, and direct use (portion of generated electricity used behind the meter) byplant, but only for those that provide reports (generally plants 1 MW and larger). That is, these
EIA data do not comprehensively account for all CHP plants, but for those that are represented,
a good deal of operational data is provided (EIA 2013a, 2018).
Hourly profiles of plant operation are even harder to come by. Operational profiles by operating
unit are available from the U.S. Environmental Protection Agency (EPA) for a subset of theplants that report on EIA Form 860. These data comes from continuous emission monitoring
systems (CEMS) (U.S. EPA 2016; EPA 2013), which are generally required for thermal plants
with nameplate capacity greater than 25 MW (EPA 2009). To assemble an hourly model of how
CHP and other behind-the-meter thermal generation operated in 2012, all these data sources were56
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brought together to estimate capacity, capacity factor (annual generation expressed as a fractionof nameplate capacity running for an entire year), and behind-the-meter fraction for all relevant,known plants by sector and by state. Hourly profiles were assigned based on selecting a profile
from the CEMS data, adjusting it to match the capacity factor, and then shifting it to fit themodeled plant’s time zone.
As explained in detail in Appendix I, our CHP model accepts the DOE CHP Installation
Database as the ground-truth regarding CHP capacity, adds to it some behind-the-meter thermal(but not CHP) capacity from the EIA data sources, and then proceeds to fill in operational details
via a matching process that accounts for such characteristics as sector, NAICS code, plant prime
mover, plant size, and plant location. A similar (but necessarily abbreviated) matching process
is applied to select a starting-point CEMS profile, which is then adjusted to match the required
capacity factor. The profile is scaled by the behind-the-meter fraction identified from the EIA
data. In the end, these profiles are aggregated up to the sector level by state, differentiated by
CHP and not-CHP. A capacity and generation summary for select states and all of the CONUS isprovided in Table 16. The starting-point CEMS profiles are shown for select weeks in Figure 15.
Table 16. Combined Heat and Power and Distributed Thermal Model: Capacity and
Behind-the-Meter Generation Summary
Behind-the-Meter
Generation (TWh)Capacity (GW)
State

Ind.

Com.

Res.

Total

Ind.

Com.

Res.

Total

TX

17.3

0.4

0.000

17.6

38.1

1.5

0.000

39.6

CA

6.8

1.9

0.002

8.7

23.3

5.3

0.005

28.6

LA

7.2

0.0

—

7.2

15.5

0.2

—

15.7

NY

3.4

2.1

0.106

5.6

14.6

6.4

0.316

21.3

MI

3.0

0.3

—

3.3

6.6

0.6

—

7.2

AL

3.2

0.0

—

3.2

8.9

0.0

—

8.9

FL

2.8

0.4

—

3.2

9.5

1.5

—

11.1

NJ

2.8

0.3

0.006

3.2

12.3

1.1

0.019

13.4

PA

2.3

0.5

0.005

2.8

6.3

1.5

0.014

7.8

IN

2.4

0.1

—

2.5

4.1

0.3

—

4.3

All
others

20.1

4.0

0.007

24.1

62.7

10.5

0.020

73.2

Total

71.2

10.2

0.125

81.5

201.8

29.0

0.375

231.2

Res. = residential, Com. = commercial, and Ind. = industrial

57
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Figure 15. CEMS CHP plant capacity factor profiles

2.3.2.2 Distributed Photovoltaics (DPV)County-level distributed PV capacity was determined by using the dGen model (Sigrin et al.
2016) to geographically disaggregate and then segment by panel tilt and orientation, state-level
historical estimates of distributed PV capacity compiled by GTM Research and the Solar Energy
Industries Association (Perea et al. 2017). The county-level capacity totals were then allocatedproportionally by population density across NSRDB geographic grid cells (Wilcox 2012), andthe System Advisor Model (NREL 2014) was used to generate profiles based on solar panel tilt
and orientation, along with the solar radiation timeseries available for each NSRDB site.
Table 17 summarizes the capacity and annual generation captured by this model of 2012 DPVin the CONUS.
58
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Table 17. Distributed Photovoltaics Capacity and Generation Summary
Capacity (MW)State

Generation (GWh)

Res.

Com.

Ind.

Total

Res.

Com.

Ind.

Total

CA

875

425

425

1,724

1,347

663

664

2,674

NJ

153

334

334

821

190

421

421

1,032

AZ

173

79

79

331

289

133

132

553

MA

34

75

75

184

41

92

92

225

PA

41

67

67

175

49

83

82

214

CO

78

45

45

169

120

68

69

257

NY

62

33

33

127

78

40

39

157

MD

31

26

26

84

40

35

34

109

All
others

178

203

203

585

232

276

281

789

Total

1,624

1,287

1,287

4,199

2,386

1,810

1,814

6,010

2.3.3 Electricity Sector Load Data
To calibrate and validate the 2012 dsgrid data set, we leverage two categories of load data thatcover the entire electricity sector from the supply side: historical hourly load data such as thoseavailable from the FERC Form 714 reports (FERC 2016) and the utility data reported on EIA
Form 861 (EIA 2013a). The data used are summarized in Table 18.
Table 18. Top-Down Data on U.S. Power System Demand Used in dsgrid to Define System Losses
and Model Residuals
Temporal
Resolution

Geographic
ExtentGeographic
Resolution

Planning area electricity
demandHourlyCONUS

FERC Planning
Region/ISO
Subregion

EIA Form 861b
Retail Sales

Retail sales by sector

AnnualU.S.

Utility-stateintersection

EIA Form 861b
Operational Data

Energy furnished or consumed
without charge, energy losses

AnnualU.S.

Utility-stateintersection

SourceFieldsFERC 714 andISO Reportingaa FERC (2016); SPP (2016); pjm (2016); MISO (2016); ISO New England (2016); NYISO (2016)b EIA (2013a)

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Hourly load data for 2012 were derived from FERC Form 714 and from the ISOs cited in Table
18. For consistency, the source data were converted to hour-ending (if not already reported assuch) and shifted to EST where necessary. To determine the load at the state level, the source
data were first disaggregated to the transmission nodes represented in the Multiregional
Modeling Working Group 2015–2026 Summer Peak load flow case in the EasternInterconnection 49 and the Western Electricity Coordinating Council (WECC) Transmission
Expansion Planning Policy Committee 2024 Common Case in the Western Interconnection(Brinkman et al. 2016). Next, the load was aggregated to the state level based on the location ofeach node derived above. Load for Texas was determined by aggregating all the nodes
mentioned above that are located in Texas along with the historical hourly load provided by theElectric Reliability Council of Texas (ERCOT 2017).
Data from two EIA Form 861 tables are used. The total amount of electricity provided by loadserving entities in 2012 is estimated by taking the retail sales by sector (residential, commercial,industrial, and transportation) from the Retail Sales table, and adding to it the energy listed in the
Operational Data table as “furnished without charge” or “consumed by respondent withoutcharge.” Per EIA (2013b), the residential sector is defined as any customers whose energy is
primarily consumed for residential end uses (i.e., “space heating, water heating, air conditioning,lighting, refrigeration, cooking, and clothes drying”); “manufacturing, construction, mining,
agriculture (irrigation), fishing, and forestry establishments” are considered to comprise theindustrial sector; and transportation is described as “railroads and railways.” Everything else isdefined as falling in the commercial sector, which therefore includes commercial buildings but
also “public street and highway lighting, municipalities, divisions or agencies of State andFederal Governments under special contracts or agreements, and other utility departments, as
defined by the pertinent regulatory agency and/or electric utility.” Because of this latterdescription, the energy listed as “furnished without charge” or “consumed by respondent withoutcharge” is ultimately added to the commercial retail sales. As shown in Table 19, more than half
this energy is furnished by just six of the 2,190 reporting utilities, and at least 8.8 TWh appears
to be associated with California water projects.
Table 19. Largest Providers of Energy Furnished or Consumed without Charge in 2012
as Reported on EIA Form 861
Furnished without Charge

Consumed by Respondent without Charge

State

Quantity(TWh)

Proportionof Total (%)

New York StateElectric and Gas
Corporation

NY

3.7

32%

Western Area Power
Administration

CO

2.9

PUD No 2 of Grant
CountyWA

Utility Name

Remaining 2,187 reporters
Total

49

State

Quantity(TWh)

California Dept. of
Water Resources

CA

7.4

42%

25%

Metropolitan Water
District of Southern
California

CA

1.4

8%

1.5

13%

Bonneville Power
Administration

OR

1.2

7%

3.6

30%

Remaining 2,187 reporters

7.5

43%

11.7

100%

17.5

100%

Utility Name

Total

Proportionof Total (%)

Data provided by Energy Visuals, Inc., http://www.energyvisuals.com/

60
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We also use the EIA Form 861 Operational Data on Total Energy Losses to inform our modelof system losses.
All these historical data are used at the state level. Although the EIA Form 861 data are natively
provided at a finer resolution (the utility-state intersection level), that level of resolution does not
map cleanly to counties. Furthermore, some of the historical hourly load data are provided at aresolution coarser than single states. For these reasons, we find it most straightforward to use
these historical data at the state level or coarser and leave it as future work to refine our
mappings between political and electrical geography.
2.3.4 Derived Models
The historical electricity sector data are used to construct two derived components of dsgrid.
There is an hourly, state-level model of power system losses (T&D), and we compute two sets ofresiduals: annual by sector by state and all electricity load hourly by state.
2.3.4.1 System Losses
T&D power system losses are the difference between the amount of electricity generated byutilities and independent generators and the amount of electricity consumed by end users as
measured by on-site electricity meters. Estimates of these losses are created and included in thedsgrid historical load data set (1) to provide a way to estimate system-level load based on thedsgrid site-level demand data, and (2) to facilitate the calculation of hourly model residuals.
The historical electricity sector data provides two estimates of system losses. There is direct
reporting of losses in the EIA Form 861 Operational data. We can also estimate system losses bysubtracting the sum of the EIA Form 861 annual energy by sector (sales plus energy furnished or
consumed without charge) from the hourly historical load data. The latter estimate falls out
because the FERC Form 714 and ISO reporting represents generation plus imports minus exports
for each reporting power system, and therefore includes all the system losses; whereas the sales
and electricity furnished statistics represent site (i.e., metered) electricity load, which does notinclude losses.

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Figure 16. Raw state-level loss factors (a) derived from utility reported losses (EIA Reported) and(b) estimated by comparing historical hourly supply and annual metered load data (FERC-EIA
estimate), ordered by (c) their difference (estimate - reported)

To develop our model of system losses, we compare these two loss estimates, both expressed asa loss factor (%) relative to the EIA annual energy reported by state. 50 This exercise reveals large
discrepancies for several states, some for which the reported EIA annual energy is greater than
the aggregated hourly load data (resulting in negative load factor estimates) and others for which
the reported EIA annual energy is much smaller than the aggregated hourly load data (resulting
in loss factors greater than 15% or even 20%) (Figure 17). Based on the provenance of the hourly
load data, it is not too surprising that the disaggregation to states is inaccurate. Perhaps more
surprising are the very low loss estimates we obtain for some states using EIA data alone (e.g.,West Virginia and Utah’s estimates are less than 1%, see Table J-1 in Appendix J.)

Per Section 2.3.3, this consists of annual sales reported by sector plus electricity furnished or consumed withoutcharge.
5062This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure 17. Loss factors for state groupings
The state groupings were developed to mitigate the greatest discrepancies seen in the raw state-level data.
The loss factors used in the dsgrid system losses model are the values shown in this figure for EIA Reported.

Given this finding, and our general knowledge of electrical system topology (e.g., the extents ofthe Eastern, Western, and ERCOT interconnections, and the footprints of ISOs/RTOs 51), we
developed groupings of states that mitigate the worst of these discrepancies. The resulting
reported and estimated loss factors are shown in Figure 17 and reported in Table 20.The dsgrid
system losses model applies the loss factors estimated using only EIA data (losses, annual sales,and energy furnished or consumed without charge) for the state groupings; that is, each state ina particular group is assigned the corresponding loss factor highlighted in Table 20. These loss
factors generally fall into a reasonable range (2.5%–9.5%), and 12 of 24 are within one
percentage point of the estimate obtained by comparing historical hourly system load andhistorical annual metered load data. The discrepancies that remain between the two loss factor
estimates point to two areas of future work: the development of better mappings between
electrical and political geography, and the development of a better/more resolved understandingof system losses. 52

RTO = regional transmission organization
Some jurisdictions, e.g., ERCOT (http://www.ercot.com/mktinfo/metering/dlfmethodology,
http://www.ercot.com/content/wcm/key_documents_lists/143946/2018_Transmission_Loss_Factors_Final.xlsx)
collect detailed information on T&D losses. From these data we see that there are real regional differences in loss
factors, and differences depending on line loadings and weather. The dsgrid system losses model relies on national
level data sets, which are subject to reporting and geographical mapping error, for expediency. Untangling thereporting and mapping errors from true regional differences, and disaggregating transmission from distribution
losses would be appropriate areas of future work.
51
52

63
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Table 20. Loss Factors Applied at the State Level to Create the System Losses Derived Model
EIA Annual
Losses(TWh)

Reported
FERC
Loss Factor Hourly Load
(%)
(TWh)

FERC – EIA
Loss
Estimated
Estimate
Loss Factor
(TWh)
(%)

State Groups

EIA Annual
Site Energy(TWh)Estimated Reported(% pt)CO, MT, UT, WY

118.1

5.5

4.6

116.2

-1.9

-1.6

-6.2

AL, FL, GA, MS

487.8

30.3

6.2

496.0

8.2

1.7

-4.5

CA, NV

304.3

13.9

4.6

309.7

5.5

1.8

-2.8

ID, OR, WA

166.1

12.2

7.3

175.7

9.6

5.8

-1.5

MI

105.0

5.6

5.4

109.4

4.3

4.1

-1.2

OH

153.0

13.1

8.5

164.5

11.5

7.5

-1.0

KS, NE, SD

83.3

5.6

6.7

88.5

5.2

6.2

-0.5

KY, WV

120.0

3.7

3.0

123.1

3.2

2.6

-0.4

VA

108.0

3.5

3.3

111.1

3.1

2.9

-0.4

LA

84.9

4.0

4.7

88.8

3.9

4.6

-0.1

PA

144.9

7.7

5.3

152.6

7.7

5.3

0.0

NC

128.4

9.4

7.3

138.0

9.6

7.5

0.1

ME, NH, VT

28.1

1.0

3.6

29.1

1.1

3.8

0.2

OK

59.9

5.6

9.4

65.8

5.8

9.8

0.4

DE_NJ

86.7

3.9

4.5

91.1

4.4

5.1

0.6

SC

78.1

3.0

3.8

81.6

3.6

4.6

0.8

TX

366.2

19.4

5.3

388.8

22.6

6.2

0.9

DC, MD

73.2

3.6

4.9

77.9

4.7

6.4

1.5

CT, MA, RI

93.0

2.4

2.6

96.9

3.9

4.2

1.6

IA, IL, IN, WI

364.2

11.0

3.0

381.2

17.0

4.7

1.7

AZ, NM

98.4

6.1

6.2

106.4

8.1

8.2

2.1

NY

147.2

5.8

3.9

163.1

15.9

10.8

6.9

MN, ND

83.0

6.1

7.3

94.8

11.8

14.2

6.9

AR, MO, TN

226.1

16.2

7.2

259.2

33.0

14.6

7.4

Data used in the derived model are highlighted.

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Finally, the hourly system losses are estimated based on a rearrangement of the expression

into

System Load(𝑡𝑡) = Metered Electricity ∙(1 + Loss Factor) ,Metered Electricity(𝑡𝑡) =This yields an estimate of hourly losses equal to:

System Load(𝑡𝑡).
1 + Loss Factor

Losses(𝑡𝑡) = System Load(𝑡𝑡) − Metered Electricity(𝑡𝑡)= System Load(𝑡𝑡) ∙Loss Factor,(1 + Loss Factor)

which we compute by state using the historical hourly system load and the loss factors
highlighted in Table 20.
2.3.4.2 Residuals
The distributed generation models described in Section 2.3.2, the historical electricity-sector data
described in Section 2.3.3, and the system losses model just described enable us to calculate
model residuals for the initial dsgrid data set for historical year 2012. As depicted in Figure 18,
two sets of residuals are calculated—one for each historical electric-sector data set. The left sideof Figure 18 shows the process of comparing the sum of the EIA Form 861 annual load data andthe distributed generation models to the dsgrid bottom-up models (detailed and gap) to computeannual residuals by state and sector. Similarly, the right side of Figure 18 outlines the process forcomputing hourly, all-electricity residuals.

Figure 18. Model residuals are calculated using electricity sector data and distributed
generation models.

65
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Mathematically, computing hourly residuals for each state starts with the following relationshipfor all electricity demand at a given time t:
System Load(𝑡𝑡) − Losses(𝑡𝑡) = Detailed Load(𝑡𝑡) + Gap Load(𝑡𝑡) −Distributed Generation(𝑡𝑡) + 𝜀𝜀𝑡𝑡 ,where system load is the total amount of generation plus net imports that must be provided by
the system, the losses model is as defined in the above section, the other time varying terms are
dsgrid model components, and 𝜀𝜀𝑡𝑡 accounts for all site-load residuals currently missed by ourmodel. We therefore calculate hourly residuals as:
𝜀𝜀𝑡𝑡 = System Load(𝑡𝑡) − Losses(𝑡𝑡) − Detailed Load(𝑡𝑡) − Gap Load(𝑡𝑡)+ Distributed Generation(𝑡𝑡).

The mathematical expression for the annual sectoral residuals is similar. For those we start fromthe expression:
Annual Retail Sales(𝑠𝑠)= Detailed Load(𝑠𝑠) + Gap Load(𝑠𝑠) − Distributed Generation(𝑠𝑠) + 𝜀𝜀𝑠𝑠 ,where s represents the sector: residential, commercial, industrial, or transportation. The sector
residuals are thus:
𝜀𝜀𝑠𝑠 = Annual Retail Sales(𝑠𝑠) − Detailed Load(𝑠𝑠) − Gap Load(𝑠𝑠) + Distributed Generation(𝑠𝑠).

Both types of residuals are calculated at the state level and analyzed in what follows.

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3 Historical Year Model Overview

The data included in this version of dsgrid are summarized in Table 21. All the components
described in Figure 18 are included; namely, the historical hourly load derived from ISO and
FERC Form 714 reporting is in the first row. Together with the third row, which is EIA Form
861 data on retail sales (plus energy furnished without charge, which has been added to thecommercial column), this is the ground truth to which we compare the core dsgrid data. The coredsgrid data consist of the bottom-up sector models plus the gap models, which, when they aresummed together, represent an estimate of site electricity use. These data are supplemented withmodels of distributed generation, and with T&D losses, to enable the calculation of residuals andthe creation of modeling-relevant aggregated load profiles.
Table 21. Summary of Contiguous U.S. Electricity Use in Terawatt-Hours, Top-Down and
Represented in dsgrid
ComponentType

Component
Name

Top-down

Hourly loada

3,910

DerivedT&D losses

199

Top-down

Annual energyb

dsgridDistributed generation

dsgrid-core

Gap models

dsgrid-core
Derived
DerivedAnnual sector residualsd

DerivedHourly residualse

Residential

Commercial

1,370

Industrial

Transport

Total

1,350

981

7

3,708

3

31

204

–

237

218

454

184

6

862

Detailed sector models

1,169

1,107

893

–

3,170

Total site energyc

1,372

1,381

1,184

7

3,945

-15

-180

107

1

-87
-126

a FERC Form 714 and independent system operator (ISO) reportingb U.S. Energy Information Administration (EIA) Form 861
c Total site energy is the top-down annual energy plus distributed generation. This is all the load we are expecting

to model with the bottom-up detailed sector and gap models.

d The sector level residuals are equal to the total site energy minus the gap and detailed sector model components.
e The hourly residuals reported in the Total column are the sum of the state-level hourly residuals, which factor in top-

down hourly load, T&D losses, distributed generation, and the dsgrid-core model components.

dsgrid model components necessary to represent site-energy use at the hourly level are shaded green.
Components that may be factored in to estimate bulk power system load are shaded blue-grey.

From this summary, we see that the order of magnitude of electricity use is similar for the
residential, commercial, and industrial sectors, at around 1,000 TWh each. Electricity used fortransportation in 2012 was about two orders of magnitude less, rounding up to 10 TWh. The
relative maturity of the dsgrid sector models is also apparent: at this highest level of aggregation,
our residential modeling is within about 1% of site energy use, whereas commercial andindustrial estimates are within 15%. Examining the hourly—rather than the sectoral—residuals,
when all the hourly residuals are summed up directly, allowing positive and negative errors tocancel out, dsgrid is overall overestimating site electricity use by about 3%.
The calculation of sectoral and hourly residuals depends on our assumptions about distributed
generation, for which there is no definitive source of ground truth. However, modeling
67
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distributed CHP, other distributed thermal generators, and distributed PV (see Section 2.3.2 andAppendix I) yields estimates of 0.2%, 2%, and 17% of sectoral electricity coming from behindthe-meter generation for the residential, commercial, and industrial sectors respectively.
We gain further insight into the model, including temporally correlated errors, by exploring thehourly timeseries data. For example, the modeled load per sector (detailed sectoral and gap) andT&D losses are plotted alongside the historical hourly load and the historical hourly load plus themodeled distributed generation in Figure 19 (next page) for four weeks, one selected from eachseason, for all of the contiguous United States. Overall, this shows that dsgrid is capturingseasonal load shapes—winter and autumn days display the double-peak pattern, whereas load inspring and summer demonstrates only one peak per day—but regularly exaggerates thedifferences between weekday and weekend energy use, as well as between daytime andnighttime energy use, the latter especially during cooling season (e.g., summer and spring).
The differences between dsgrid data and the historical load data are more clearly visible in
Figure 20, which is structured similarly to Figure 19, but with the data grouped to explicitly plot
the portion of load met by distributed generation and the hourly residuals. When the hourly
residuals are positive they represent missing load, we refer to them as “underage,” and they areplotted between the modeled load and system loss components. Negative residuals represent
overestimates of load; these are referred to as “overage” and are plotted above the historicalhourly load plus DG line. In addition to explicitly showing the residuals that are implicit in
Figure 19, these plots verify the correctness of our residuals calculations and demonstrate how
the component categories of the dsgrid model fit together. Regional versions of Figure 19 and
Figure 20 are available in Appendix G.

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Figure 19. Bottom-up detailed sectoral model and gap model load compared to bulk-level
historical hourly load for all of the CONUS in 2012, for four representative weeks

69
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Figure 20. dsgrid hourly residuals plotted along with the bottom-up model data, T&D loss
estimates, and historical bulk power load data, for the contiguous United States

To get a clearer understanding of model fit, we analyze the hourly and sectoral residuals
computed at different geographic, temporal, and sectoral resolutions. To do this, the dsgrid
model is first aggregated to different temporal and geographic resolutions. Temporally, wesummarize the size of the all-electricity hourly residuals at the following levels of aggregation,with all aggregations based on EST:
•

Hour

•

Day

•

Week

•

Season (based on solar solstices and equinoxes)

•

Year.
70

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Geographically, we summarize both types of residuals at these levels of aggregation:•

State (48 in the contiguous United States)•

State groups (24 from the loss model component, Table 20)•

Census division (9 in total, see Appendix G)•

Census region (4 in total, see Appendix G)•

CONUS.

To create summary fit statistics, we rely on relative absolute error metrics, suitably averaged
over the relevant dimensions. For the hourly residuals, there is no sectoral dimension and we
begin by calculating mean relative absolute error (MRAE) over the temporal dimension,
calculating one MRAE for each geographical unit g:

MRAE𝑔𝑔 =�𝜀𝜀𝑡𝑡,𝑔𝑔 �
𝐸𝐸𝑡𝑡,𝑔𝑔, 𝐸𝐸𝑡𝑡,𝑔𝑔 = System Load(𝑡𝑡, 𝑔𝑔) − Losses(𝑡𝑡, 𝑔𝑔) + Distributed Generation(𝑡𝑡, 𝑔𝑔)
∑𝑡𝑡 1

∑𝑡𝑡

This statistic is then inverted into a measure of fit:

(MRAE Fit)𝑔𝑔 = 1 − MRAE𝑔𝑔 ,and averaged over the geographical units using the annual site energy as the weighting factor:
MRAE Fit =∑𝑔𝑔 (MRAE Fit)𝑔𝑔 ∙ ∑𝑡𝑡 𝐸𝐸𝑡𝑡,𝑔𝑔.
∑𝑔𝑔,𝑡𝑡 𝐸𝐸𝑡𝑡,𝑔𝑔

For the sectoral residuals, there is no temporal dimension. For the individual sectors, wetherefore have a basic relative absolute error (RAE) statistic for each geographical unit g:
RAE𝑠𝑠,𝑔𝑔 =�𝜀𝜀𝑠𝑠,𝑔𝑔 �,
𝐸𝐸𝑠𝑠,𝑔𝑔

𝐸𝐸𝑠𝑠,𝑔𝑔 = Annual Electricity(𝑠𝑠, 𝑔𝑔) + Distributed Generation(𝑠𝑠, 𝑔𝑔).

and if we convert to a fit metric and perform an energy-weighted average we get:

(RAE Fit)𝑠𝑠 =(RAE Fit)𝑠𝑠,𝑔𝑔 = 1 − RAE𝑠𝑠,𝑔𝑔 ,∑𝑔𝑔 (RAE Fit)𝑠𝑠,𝑔𝑔 ∙ 𝐸𝐸𝑠𝑠,𝑔𝑔 ∑𝑔𝑔�𝐸𝐸𝑠𝑠,𝑔𝑔 − �𝜀𝜀𝑠𝑠,𝑔𝑔 ��=.
∑𝑔𝑔 𝐸𝐸𝑠𝑠,𝑔𝑔∑𝑔𝑔 𝐸𝐸𝑠𝑠,𝑔𝑔

To calculate overall fit across all sectors, the final formula is leveraged to compute:(RAE Fit)𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 =∑𝑔𝑔�∑𝑠𝑠 𝐸𝐸𝑠𝑠,𝑔𝑔 − �∑𝑠𝑠 𝜀𝜀𝑠𝑠,𝑔𝑔 ��.
∑𝑔𝑔,𝑠𝑠 𝐸𝐸𝑠𝑠,𝑔𝑔
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The resulting fit statistics are plotted in Figure 21.

Figure 21. dsgrid fit statistics for total site energy as a function of geographic, temporal,
and sectoral resolution

First examining the hourly electricity residuals, we see visual confirmation of what we saw
in Table 21, namely that in total (annual values for the contiguous United States), the dsgrid
bottom-up models capture the site energy represented in the historical hourly load data combinedwith the dsgrid distributed generation models to within a relative error of 4%. However, if we
examine the model at the most resolved level for which hourly residuals are available (state-level
and hourly), the level of fit drops almost to 80%. Where is most of the fit lost? Temporally, thefirst large gap occurs when we go from weeks to days; this likely speaks to the weekdayweekend discrepancy apparent in Figure 19 and Figure 20. The daytime-to-nighttime shifts
similarly show up in the difference between the hour and day curves. The load duration curves 53
in Figure 22, which represent the hourly CONUS data, are another confirmation of these trends.
Specifically, consistent with its provenance as a confederation of sectoral energy models
historically focused on annual energy use metrics, dsgrid is capturing the total amount of load
reasonably well, but it contains significantly more load variability than the historical data.
Geographically, there is a large difference between census divisions and census regions, and amore modest, but significant difference between states and state groups. For these hourly
residuals, which rely on a disaggregation of system load data reported by ISO region or FERC
Form 714 planning region, a significant portion of the error introduced by disaggregating fromthe State Group to State level may be attributable to that process (and not to the bottom-up
modeling alone).

53

Load duration curves are constructed by sorting timeseries load data in descending order and plotting the result.

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Examining the sectoral residuals, the relative maturity of the residential modeling is immediately
apparent, as its level of agreement with the total annual energy use reported by state on EIA
Form 861 plus our distributed generation estimates is above 92% at all levels of disaggregation(down to states). The level of fit for all other sectors is below 85% at the state level, but it
is greater than 80% for the other two main electricity-consuming sectors: commercial
and industrial.

Figure 22. Historical and dsgrid load duration curves for the CONUS in 2012
The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of thedetailed sector and gap models.

The detailed sector models provide information on both subsector and end-use breakdowns down
to the county, hourly level. The annual CONUS summary for the residential detailed (i.e.,
ResStock and midrise apartments from ComStock) and gap models is in Table 22 in absoluteterms (GWh). Table 23 reports the relative proportions of electricity by end use per subsector,
and of electricity use by subsector for the entire residential sector. The diurnal load shapes forthe sector, broken down by end use, and averaged over each season and weekday/weekend
combination are shown in Figure 23.

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Table 22. Residential Subsectors, Summary of Electricity by End Use for the CONUS in 2012
Interior
Space
Equipment Cooling
(GWh)
(GWh)

Subsector

Interior
Lights(GWh)

Space
Heating
(GWh)

Fans
(GWh)

Water
Systems(GWh)

Exterior
Lights(GWh)138,204

131,556

101,198

95,84628,036

1,342

Pumps
(GWh)

Heat
Rejection
(GWh)

Total
(GWh)

Single Family Detached

430,941 223,229

Mobile Home

36,64618,185

7,707

7,690

7,229

9,211

1,254

47

—

87,970Apartment in Building
2 to 4 Units

22,346

8,805

9,275

4,324

9,158

—

11,158780

279

66,125Single Family Attached28,485

11,454

6,075

5,306

5,065

6,326

990

97

—

63,797

Midrise Apartment
Building a

6,174

2,503

2,720

1,236

2,641

—

3,247

250

87

18,858524,593 264,176

163,980

150,113125,290111,383

44,685

2,516

Total

— 1,150,352

366 1,387,102

a The current version of ComStock does not model water heating. See Footnote 54 for more information.

Table 23. Residential Electricity Proportions by Subsector and End Use for the CONUS in 2012
Interior
Equipment
(%)

Space
Cooling(%)

Interior
Lights(%)

Space
Heating(%)

Single Family Detached

37.5

19.4

12.0

11.4

Mobile Home

41.7

20.7

8.8

Apartment in Building
2 to 4 Units

33.8

13.3

Single Family Attached

44.6

Midrise Apartment
Building a
Total

Subsector

Water
Systems(%)

Exterior
Lights
(%)

Pumps
(%)

Heat
Rejection(%)

Total (%)

8.8

8.3

2.4

0.1

—

82.9

8.7

8.2

10.5

1.4

0.1

—

6.3

14.0

6.5

13.8

—

16.9

1.2

0.4

4.8

18.0

9.5

8.3

7.9

9.9

1.6

0.2

—

4.6

32.7

13.3

14.4

6.6

14.0

—

17.2

1.3

0.5

1.4

37.8

19.0

11.8

10.8

9.0

8.0

3.2

0.2

0.0

100.0

Fans
(%)

a The current version of ComStock does not model water heating. See Footnote 54 for more information.

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These results show that on the continental scale, ResStock and ComStock (for midrise apartmentbuildings) find interior equipment to be the largest group of loads in residences, with a relatively
flat profile. The next largest end uses (space cooling, interior lighting, and space heating) show
stronger seasonal and diurnal patterns. Fan, exterior lighting and pumping energy is more
prominent in multifamily than single-family buildings. As shown in Appendix G, the relative
importance of these end uses varies considerably across regions; however, interior equipment is
the largest end use across all the census divisions, comprising 33% (West South Central) to 48%(New England) of residential energy use.

Figure 23. Residential electricity use diurnal patterns by season, modeled for the CONUS in 201275
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End-use information is only provided for the commercial building subsectors that are modeled
in detail. 54 The annual summaries for the CONUS are provided in Table 24 and Table 25 on
an absolute basis and a proportional basis respectively. These data demonstrate considerable
differences in the relative importance of end uses depending on the commercial building typethat is being modeled. For example, although interior equipment is the largest end use for large
offices, the largest subsector in the stock model, it is not the largest end use for the sector as awhole. That distinction instead falls to interior lighting, largely based on its importance in retailbuildings, both standalone buildings and strip malls. Also, while significant work is still to
be done to calibrate ComStock, especially regarding capturing building types that appear
underrepresented in the CoStar data (e.g., schools, government offices, and other publicbuildings), the stock model does demonstrate the relative importance of office and retailbuildings, as compared to building types such as hotels and restaurants, when accounting
for U.S. electricity use.
Table 24. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End Use
for the CONUS in 2012

Subsector

Interior
Lights(GWh)Interior
Equipment
(GWh)

Fans
(GWh)

Space Exterior
Cooling Lights
(GWh)
(GWh)

Space
Heating
(GWh)

Pumps
(GWh)

Heat
Rejection
(GWh)

Total
(GWh)

Large Office

79,566

92,373

61,20153,542

16,165

1,342

7,537

2,822

314,548

Strip Mall

170,722

24,812

38,24822,08034,871

7,583

131

54

298,501

Standalone RetailStore72,45622,961

22,286

14,38911,016

7,126

57

23

150,314

Medium Office

19,49022,237

14,368

11,388

6,934

741

789

275

76,222

Small Office

15,961

21,861

10,893

8,286

6,235

1,424

80

50

64,791

Warehouse

20,906

8,321

7,058

1,984

10,120

6,280

49

26

54,742

Full Service
Restaurant

9,906

22,215

5,592

4,634

2,767

914

56

20

46,105Large Hotel

9,188

14,671

8,412

7,994

2,309

499

463

164

43,700

Hospital

6,702

8,875

4,455

4,686

513

8

775

359

26,373

Primary School

4,759

3,796

2,241

1,871

612

421

77

37

13,813Outpatient
Treatment Facility

3,726

5,503

1,504

987

1,334

—

170

76

13,299Small Hotel

1,075

1,508

147

112

716

87

14

5

3,664

Quick Service
Restaurant

88

521

174

126

52

4

1

1

966

249,654 176,579

132,077

93,644

26,43010,198

3,912

1,107,037

Total

414,544

In addition, information on electricity used for water heating is not provided in the current version of ComStock.
According to the 2012 CBECS, only 4% of energy used for water heating in commercial buildings comes fromelectricity, that is, about 6 TWh of the total 148 TWh (507 trillion BTU) for the entire United States. This end use
will be modeled in future versions of ComStock.
54

76
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table 25. Commercial Electricity Proportions of Detailed Models by Subsector and End Use
for the CONUS in 2012

Subsector

Interior
Space ExteriorInterior
Equipment
Cooling Lights
Lights (%)
(%)
(%)
(%)
Fans (%)

Space
Heating
(%)

Pumps
(%)

Heat
Rejection(%)

Total (%)

Large Office

25.3

29.4

19.5

17.0

5.1

0.4

2.4

0.9

28.4

Strip Mall

57.2

8.3

12.8

7.4

11.7

2.5

0.0

0.0

27.0

Standalone Retail
Store

48.2

15.3

14.8

9.6

7.3

4.7

0.0

0.0

13.6

Medium Office

25.6

29.2

18.9

14.9

9.1

1.0

1.0

0.4

6.9

Small Office

24.6

33.7

16.8

12.8

9.6

2.2

0.1

0.1

5.9

Warehouse

38.2

15.2

12.9

3.6

18.5

11.5

0.1

0.0

4.9

Full Service
Restaurant

21.5

48.2

12.1

10.1

6.0

2.0

0.1

0.0

4.2

Large Hotel

21.0

33.6

19.2

18.3

5.3

1.1

1.1

0.4

3.9

Hospital

25.4

33.7

16.9

17.8

1.9

0.0

2.9

1.4

2.4

Primary School

34.5

27.5

16.2

13.5

4.4

3.0

0.6

0.3

1.2

Outpatient
Treatment Facility

28.0

41.4

11.3

7.4

10.0

—

1.3

0.6

1.2

Small Hotel

29.3

41.2

4.0

3.1

19.5

2.4

0.4

0.2

0.3

Quick Service
Restaurant

9.1

53.9

18.0

13.0

5.3

0.4

0.1

0.1

0.1

Total

37.4

22.6

16.0

11.9

8.5

2.4

0.9

0.4

100.0

The seasonal and diurnal patterns of the detailed commercial building models’ electricity use areshown in Figure 24. Large commercial buildings are generally more isolated from environmental
conditions, and they use less daylight than residential buildings, such that we see a more
prominent role for interior lighting and fans, and relatively less heating and cooling as compared
to Figure 23. As modeled, some commercial building types also use significant quantities ofelectricity for exterior lighting. After further model calibration, we may find this result to be anoverstatement given the Buccitelli et al. (2017) estimate of 33 TWh for the exterior lighting ofcommercial and industrial buildings altogether as compared to our estimate of 93 TWh forcommercial buildings alone. Based on the overall dsgrid validation results and the end-use
shapes shown here, commercial building lighting, space cooling, and interior equipment
schedules are strong candidates for additional sectoral calibration, both diurnally and by
weekday/weekend.
Industrial manufacturing electricity use, as modeled by IGATE-E, is summarized in Table 26
and Table 27 (following pages). This sector is even more diffuse than the commercial sector. One
subsector alone (Iron and Steel Mills and Ferroalloy Manufacturing) accounts for more than 10%of electricity use. Four additional industries (Petroleum and Coal Products Manufacturing; Basic
Chemical Manufacturing; Pulp, Paper, and Paperboard Mills; Plastics Product Manufacturing) use
more than 5% of sectoral electricity. Together, these five industries comprise about 37% of total
electricity use; the remaining 63% is spread out over an additional 81 categories.
The largest electrical end use, according to our model, is machine drive, which accounts for 449
TWh of the total of 893 TWh. However, some manufacturing industries’ electricity use isdominated by process heating, for example, Iron and Steel Mills and Ferroalloy Manufacturing,
77
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Foundries, and Nonferrous Metal (except Aluminum) Production and Processing. Semiconductor
and Other Electronic Component Manufacturing stands out for facility HVAC being as important
an electrical end use as machine drive. The diurnal patterns modeled by IGATE-E, by end use
and taken in total across all of manufacturing, are shown in Figure 25. Based on dsgrid’s
underprediction of weekend energy use, the weekday/weekend scheduling shown here is anotherpotential source of error that the dsgrid team will investigate moving forward, in tandem withsimilar calibrations for residential and commercial buildings.
dsgrid also provides load shapes and limited sectoral and end-use information for its gap models(commercial, industrial, transportation, municipal water, and outdoor lighting) and distributed
generation models. CONUS-level summary data for these components are presented inAppendix F. Some analogous information is also presented for these components at the census
division level in Appendix G.

Figure 24. Commercial electricity use diurnal patterns by season, as modeled by ComStockfor the CONUS in 201278
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Facility HVAC (GWh)Electro Chemical
Processes (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)

Other Facility
Support (GWh)

Conventional Boiler
Use (GWh)

End Use Not
Reported (GWh)

Onsite Transportation(GWh)

25,74033,045

5,981

21,982

1,422

4,199

1,489

1,148

450

1,883

157

264

97,760Petroleum and Coal Products Manufacturing

54,092

2,264

2,538

971

2,824

1,690

476

656

498

750

7

69

66,835

Basic Chemical Manufacturing

33,489

2,156

3,733

9,943

5,639

2,406

900

705

1,245

803

149

148

61,316

Pulp, Paper, and Paperboard Mills

45,452

2,361

2,639

493

1,005

2,306

2,028

645

2,770

301

122

Plastics Product Manufacturing

25,843

7,733

4,880

—

3,862

4,562

—

1,099

241

—

207

—

48,426

Pharmaceutical and Medicine Manufacturing

21,874

1,412

2,463

6,586

3,735

1,585

596

467

824

532

99

98

40,272

Converted Paper Product Manufacturing

29,841

1,553

1,745

327

667

1,524

1,345

428

1,837

199

81

—

39,546

Animal Slaughtering and Processing

9,820

973

1,909

51

5,963

1,680

412

456

699

504

211

95

22,773

Pesticide, Fertilizer, and Other Agricultural
Chemical Manufacturing

11,556

749

1,320

3,549

2,013

848

321

252

444

287

53

53

21,445Resin, Synthetic Rubber, and Artificial Synthetic Fibers
and Filaments Manufacturing

11,503

741

1,284

3,420

1,940

827

310

243

428

276

51

51

21,073

Other Chemical Product and Preparation Manufacturing

9,734

627

1,089

2,904

1,647

701

263

206

364

235

44

43

17,856

Motor Vehicle Parts Manufacturing

6,810

1,935

3,307

114

889

2,214

867

697

147

276

248

211

17,716

Other Nonmetallic Mineral Product Manufacturing

8,279

3,929

1,003

274

601

767

334

237

138

—

59

25

15,645

Other Wood Product Manufacturing

11,436

971

1,154

45

110

922

166

244

274

167

38

52

15,580

Semiconductor and other Electronic
Component Manufacturing

3,472

1,806

3,648

342

1,736

1,390

1,107

738

132

327

39

290

15,027

Printing and Related Support Activities

7,800

601

2,454

164

1,078

1,224

207

406

122

—

158

—

14,213

Foundries

3,574

4,577

823

3,012

195

578

204

157

62

258

21

36

13,497

Nonferrous Metal (except Aluminum) Production
and Processing

3,496

4,447

785

2,846

184

553

193

149

58

244

20

34

13,010Soap, Cleaning Compound, and Toilet
Preparation Manufacturing

6,995

453

797

2,140

1,214

512

194

152

268

173

32

32

12,961Other Fabricated Metal Product Manufacturing

5,707

1,954

2,029

505

422

1,278

470

437

—

—

89

26

12,917

Architectural and Structural Metals Manufacturing

5,263

1,806

1,888

471

394

1,188

439

408

—

—

83

25

11,965

Dairy Product Manufacturing

5,081

502

973

26

3,020

857

209

231

354

255

107

48

11,663Aerospace Product and Parts Manufacturing

4,147

1,178

2,011

69

540

1,347

527

424

89

168

151

128

10,77979
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)

Process Heating(GWh)

Iron and Steel Mills and Ferroalloy Manufacturing

Subsector

Other Nonprocess
Use (GWh)

Machine Drive (GWh)

Table 26. Industrial Manufacturing Subsectors, Summary of IGATE-E Electricity Results by End Use for the CONUS in 2012

60,122

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)

Other Facility
Support (GWh)

Conventional Boiler
Use (GWh)

End Use Not
Reported (GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

21

2,522

710

174

193

296

213

89

40

9,628

Beverage Manufacturing

3,322

393

1,088

19

1,815

860

229

305

148

705

188

40

9,113

Glass and Glass Product Manufacturing

4,633

2,192

555

151

332

425

184

131

76

—

32

14

8,725

Cement and Concrete Product Manufacturing

4,593

2,183

560

153

337

428

187

133

77

—

33

14

8,698

Navigational, Measuring, Electromedical, and Control
Instruments Manufacturing

1,965

1,026

2,097

198

1,004

797

641

427

76

189

23

168

8,610

Paint, Coating, and Adhesive Manufacturing

4,518

292

514

1,379

782

330

125

98

173

111

21

20

8,363

Motor Vehicle Manufacturing

3,130

893

1,545

54

418

1,032

407

328

69

130

117

99

8,221

Bakeries and Tortilla Manufacturing

3,532

350

682

18

2,127

601

147

163

249

180

75

34

8,158

Other Food Manufacturing

3,424

339

659

17

2,050

581

142

157

240

173

72

33

7,888

Rubber Product Manufacturing

4,021

1,204

762

—

604

712

—

172

38

—

32

—

7,544

Alumina and Aluminum Production and Processing

1,998

2,545

451

1,638

106

318

111

86

34

140

12

20

7,457

Coating, Engraving, Heat Treating, and Allied Activities

3,229

1,111

1,171

294

245

736

274

254

—

—

52

15

7,381

Steel Product Manufacturing from Purchased Steel

1,848

2,353

416

1,510

98

293

102

79

31

129

11

18

6,888

Other Electrical Equipment and Component
Manufacturing

1,775

1,456

1,048

492

406

621

231

143

29

—

44

—

6,245

Fiber, Yarn, and Thread Mills

3,698

530

920

0

228

452

112

92

127

—

54

—

6,214

Grain and Oilseed Milling

2,643

261

509

13

1,586

449

110

121

186

134

56

25

6,094

Veneer, Plywood, and Engineered Wood Product
Manufacturing

4,461

377

442

17

42

354

63

93

104

63

14

20

6,051

Animal Food Manufacturing

2,555

254

501

13

1,570

440

108

120

184

133

55

25

5,960

Other General Purpose Machinery Manufacturing

2,480

575

1,215

60

241

829

138

219

57

—

60

—

5,873

Other Miscellaneous Manufacturing

1,932

681

1,421

41

420

819

111

286

94

—

34

14

5,854

Forging and Stamping

2,408

825

860

214

179

541

200

186

—

—

38

11

5,462

Communications Equipment Manufacturing

Total (GWh)Electro Chemical
Processes (GWh)807

Facility HVAC (GWh)411

Process Heating
(GWh)

4,150

Machine Drive (GWh)

Fruit and Vegetable Preserving and Specialty
Food Manufacturing

Subsector

967

506

1,039

98

499

395

318

212

38

94

11

83

4,260

Fabric Mills

2,387

343

602

0

150

295

73

61

84

—

35

—

4,030

Household and Institutional Furniture and Kitchen
Cabinet Manufacturing

1,962

146

806

15

66

667

—

134

48

—

44

19

3,905

Engine, Turbine, and Power Transmission
Equipment Manufacturing

1,500

346

725

36

143

495

82

130

34

—

35

—

3,527

80
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)

Other Facility
Support (GWh)

Conventional Boiler
Use (GWh)

End Use Not
Reported (GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

35

140

485

80

128

33

—

35

—

3,457

Agriculture, Construction, and Mining
Machinery Manufacturing

1,454

335

699

35

138

478

79

125

33

—

34

—

3,411

Motor Vehicle Body and Trailer Manufacturing

1,279

364

625

22

168

418

164

132

28

52

47

40

3,339

922

760

556

263

217

328

123

77

16

—

23

—

3,286

1,347

312

657

33

130

448

75

118

31

—

32

—

3,183

992

349

722

21

212

417

56

145

48

—

17

7

2,986

Commercial and Service Industry
Machinery Manufacturing

1,225

283

592

29

117

405

67

106

28

—

29

—

2,880

Sugar and Confectionery Product Manufacturing

1,238

123

241

6

755

212

52

58

89

64

27

12

2,877

Electrical Equipment Manufacturing
Metalworking Machinery Manufacturing
Medical Equipment and Supplies Manufacturing

Computer and Peripheral Equipment Manufacturing

Total (GWh)Electro Chemical
Processes (GWh)710

Facility HVAC (GWh)339

Process Heating
(GWh)

1,471

Machine Drive (GWh)

Industrial Machinery Manufacturing

Subsector

640

337

703

67

340

266

217

145

26

64

8

57

2,869

Lime and Gypsum Product Manufacturing

1,375

653

167

46

100

128

56

40

23

—

10

4

2,601

Textile and Fabric Finishing and Fabric Coating Mills

1,461

211

370

0

93

182

45

37

52

—

22

—

2,473

899

258

453

16

123

302

120

97

20

38

35

29

2,392

1,214

161

299

7

152

244

10

56

113

—

57

—

2,313

968

333

350

88

73

220

82

76

—

—

15

5

2,209

Ship and Boat Building
Other Textile Product Mills
Machine Shops; Turned Product; and Screw, Nut,and Bolt Manufacturing
Clay Product and Refractory Manufacturing

1,113

530

137

38

82

104

46

32

19

—

8

3

2,112

Spring and Wire Product Manufacturing

913

314

331

83

69

208

77

72

—

—

15

4

2,088

Other Transportation Equipment Manufacturing

781

222

382

13

103

255

100

81

17

32

29

24

2,039

Ventilation, Heating, Air-Conditioning, and Commercial
Refrigeration Equipment Manufacturing

622

144

305

15

61

208

35

55

14

—

15

—

1,475

Household Appliance Manufacturing

410

336

241

113

93

143

53

33

7

—

10

—

1,437

Manufacturing and Reproducing Magnetic and
Optical Media

294

153

310

29

148

118

94

63

11

28

3

25

1,275

Railroad Rolling Stock Manufacturing

453

130

225

8

61

150

60

48

10

19

17

14

1,196

Seafood Product Preparation and Packaging

511

51

100

3

315

88

22

24

37

27

11

5

1,194

Boiler, Tank, and Shipping Container Manufacturing

478

163

166

41

34

105

38

35

—

—

7

2

1,069

Office Furniture (including Fixtures) Manufacturing

501

37

208

4

17

172

—

35

12

—

11

5

1,003

Audio and Video Equipment Manufacturing

205

107

216

20

103

82

66

44

8

19

2

17

888

81
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Other Facility
Support (GWh)

Conventional Boiler
Use (GWh)

End Use Not
Reported (GWh)

Onsite Transportation
(GWh)

867

19

4

—

6

—

817

27

17

—

6

—

786

7

10

12

7

2

2

666

20

18

—

—

4

1

540

2

11

21

—

11

—

431

—

9

3

—

3

1

268

1

5

1

—

1

—

209

1

2

1

—

0

—

76

0

1

0

—

0

—

43

3

1

1

—

—

0

0

31

2

—

0

0

—

0

—

14

—

2

81

30

14

135

—

2

5

39

21

18

54

1

29

46

1

4

45

21

1

8

23

8

0

3

8

3

4

0

2

5

14

5

5

1

1

6

1

4

—

0

1

0

1

—

0

0

—

0

0

—

0

449,086 106,835

82,372

67,81563,21856,031

19,226

16,901

14,683

10,449

4,030

103

2

Electric Lighting Equipment Manufacturing

233

191

137

64

Cut and Sew Apparel Manufacturing

319

39

229

—

Sawmills and Wood Preservation

488

42

49

Cutlery and Handtool Manufacturing

237

81

85

Textile Furnishings Mills

225

30

56

Other Furniture Related Product Manufacturing

136

10

55

Other Leather and Allied Product Manufacturing

132

17

Footwear Manufacturing

48

6

Leather and Hide Tanning and Finishing27

Hardware Manufacturing
Apparel Accessories and Other Apparel Manufacturing

82
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)

Process Heating
(GWh)
38

Other Nonprocess
Use (GWh)

53

Facility HVAC (GWh)Other Process Use
(GWh)

4

Facility Lighting
(GWh)

18

Process Cooling And
Refrigeration (GWh)

66

22

320

Total

14

82

Tobacco Manufacturing

Apparel Knitting Mills

29

171

Machine Drive (GWh)

Subsector

2,702 893,346

Other Facility
Support (%)

Conventional
Boiler Use (%)

End Use Not
Reported (%)

OnsiteTransportation
(%)

Other
Nonprocess
Use (%)

4.3

1.5

1.2

0.5

1.9

0.2

0.3

10.9

3.8

1.5

4.2

2.5

0.7

1.0

0.7

1.1

0.0

0.1

7.5

6.1

16.2

9.2

3.9

1.5

1.2

2.0

1.3

0.2

0.2

6.9

3.9

4.4

0.8

1.7

3.8

3.4

1.1

4.6

0.5

0.2

—

6.7

16.0

10.1

—

8.0

9.4

—

2.3

0.5

—

0.4

—

5.4

3.5

6.1

16.4

9.3

3.9

1.5

1.2

2.0

1.3

0.2

0.2

4.5

3.9

4.4

0.8

1.7

3.9

3.4

1.1

4.6

0.5

0.2

—

4.4

43.1

4.3

8.4

0.2

26.2

7.4

1.8

2.0

3.1

2.2

0.9

0.4

2.5

Pesticide, Fertilizer, and Other Agricultural
Chemical Manufacturing

53.9

3.5

6.2

16.6

9.4

4.0

1.5

1.2

2.1

1.3

0.2

0.2

2.4

Resin, Synthetic Rubber, and Artificial Synthetic Fibers
and Filaments Manufacturing

54.6

3.5

6.1

16.2

9.2

3.9

1.5

1.2

2.0

1.3

0.2

0.2

2.4

Other Chemical Product and Preparation Manufacturing

54.5

3.5

6.1

16.3

9.2

3.9

1.5

1.2

2.0

1.3

0.2

0.2

2.0

Motor Vehicle Parts Manufacturing

38.4

10.9

18.7

0.6

5.0

12.5

4.9

3.9

0.8

1.6

1.4

1.2

2.0

Other Nonmetallic Mineral Product Manufacturing

52.9

25.1

6.4

1.8

3.8

4.9

2.1

1.5

0.9

—

0.4

0.2

1.8

Other Wood Product Manufacturing

73.4

6.2

7.4

0.3

0.7

5.9

1.1

1.6

1.8

1.1

0.2

0.3

1.7

Semiconductor and Other Electronic
Component Manufacturing

23.1

12.0

24.3

2.3

11.6

9.2

7.4

4.9

0.9

2.2

0.3

1.9

1.7

Printing and Related Support Activities

54.9

4.2

17.3

1.2

7.6

8.6

1.5

2.9

0.9

—

1.1

—

1.6

Foundries

26.5

33.9

6.1

22.3

1.4

4.3

1.5

1.2

0.5

1.9

0.2

0.3

1.5

Nonferrous Metal (except Aluminum) Production
and Processing

26.9

34.2

6.0

21.9

1.4

4.3

1.5

1.1

0.4

1.9

0.2

0.3

1.5

Soap, Cleaning Compound, and Toilet Preparation
Manufacturing

54.0

3.5

6.1

16.5

9.4

4.0

1.5

1.2

2.1

1.3

0.2

0.2

1.5

Other Fabricated Metal Product Manufacturing

44.2

15.1

15.7

3.9

3.3

9.9

3.6

3.4

—

—

0.7

0.2

1.4

Architectural and Structural Metals Manufacturing

44.0

15.1

15.8

3.9

3.3

9.9

3.7

3.4

—

—

0.7

0.2

1.3

Dairy Product Manufacturing

43.6

4.3

8.3

0.2

25.9

7.4

1.8

2.0

3.0

2.2

0.9

0.4

1.3

Aerospace Product and Parts Manufacturing

38.5

10.9

18.7

0.6

5.0

12.5

4.9

3.9

0.8

1.6

1.4

1.2

1.2

Fruit and Vegetable Preserving and Specialty Food
Manufacturing

43.1

4.3

8.4

0.2

26.2

7.4

1.8

2.0

3.1

2.2

0.9

0.4

1.1

Beverage Manufacturing

36.5

4.3

11.9

0.2

19.9

9.4

2.5

3.3

1.6

7.7

2.1

0.4

1.0

26.3

33.8

Petroleum and Coal Products Manufacturing

80.9

3.4

Basic Chemical Manufacturing

54.6

3.5

Pulp, Paper, and Paperboard Mills

75.6

Plastics Product Manufacturing

53.4

Pharmaceutical and Medicine Manufacturing

54.3

Converted Paper Product Manufacturing

75.5

Animal Slaughtering and Processing

83
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Total (%)

Other Process
Use (%)

1.5

Iron and Steel Mills and Ferroalloy Manufacturing

Subsector

Process
Cooling And
Refrigeration(%)
Facility
Lighting (%)

ElectroChemicalProcesses (%)
22.5

Process
Heating (%)

6.1

Machine Drive(%)

Facility HVAC(%)

Table 27. Industrial Manufacturing Electricity Proportions for Subsectors and End Uses as Modeled for the CONUS in 2012 by IGATE-E

Facility HVAC(%)

ElectroChemicalProcesses (%)

Other Process
Use (%)

Other Facility
Support (%)

Conventional
Boiler Use (%)

End Use Not
Reported (%)

OnsiteTransportation
(%)

Other
Nonprocess
Use (%)

25.1

6.4

1.7

3.8

4.9

2.1

1.5

0.9

—

0.4

0.2

1.0

52.8

25.1

6.4

1.8

3.9

4.9

2.1

1.5

0.9

—

0.4

0.2

1.0

Navigational, Measuring, Electromedical, and Control
Instruments Manufacturing

22.8

11.9

24.4

2.3

11.7

9.3

7.4

5.0

0.9

2.2

0.3

1.9

1.0

Paint, Coating, and Adhesive Manufacturing

54.0

3.5

6.1

16.5

9.4

3.9

1.5

1.2

2.1

1.3

0.2

0.2

0.9

Motor Vehicle Manufacturing

38.1

10.9

18.8

0.7

5.1

12.6

5.0

4.0

0.8

1.6

1.4

1.2

0.9

Bakeries and Tortilla Manufacturing

43.3

4.3

8.4

0.2

26.1

7.4

1.8

2.0

3.1

2.2

0.9

0.4

0.9

Other Food Manufacturing

43.4

4.3

8.4

0.2

26.0

7.4

1.8

2.0

3.0

2.2

0.9

0.4

0.9

Rubber Product Manufacturing

53.3

16.0

10.1

—

8.0

9.4

—

2.3

0.5

—

0.4

—

0.8

Alumina and Aluminum Production and Processing

26.8

34.1

6.0

22.0

1.4

4.3

1.5

1.1

0.4

1.9

0.2

0.3

0.8

Coating, Engraving, Heat Treating, and Allied Activities

43.7

15.1

15.9

4.0

3.3

10.0

3.7

3.4

—

—

0.7

0.2

0.8

Steel Product Manufacturing from Purchased Steel

26.8

34.2

6.0

21.9

1.4

4.3

1.5

1.1

0.4

1.9

0.2

0.3

0.8

Other Electrical Equipment and Component
Manufacturing

28.4

23.3

16.8

7.9

6.5

9.9

3.7

2.3

0.5

—

0.7

—

0.7

Fiber, Yarn, and Thread Mills

59.5

8.5

14.8

0.0

3.7

7.3

1.8

1.5

2.0

—

0.9

—

0.7

Grain and Oilseed Milling

43.4

4.3

8.4

0.2

26.0

7.4

1.8

2.0

3.1

2.2

0.9

0.4

0.7

Veneer, Plywood, and Engineered Wood Product
Manufacturing

73.7

6.2

7.3

0.3

0.7

5.9

1.0

1.5

1.7

1.0

0.2

0.3

0.7

Animal Food Manufacturing

42.9

4.3

8.4

0.2

26.3

7.4

1.8

2.0

3.1

2.2

0.9

0.4

0.7

Other General Purpose Machinery Manufacturing

42.2

9.8

20.7

1.0

4.1

14.1

2.4

3.7

1.0

—

1.0

—

0.7

Other Miscellaneous Manufacturing

33.0

11.6

24.3

0.7

7.2

14.0

1.9

4.9

1.6

—

0.6

0.2

0.7

Forging and Stamping

44.1

15.1

15.7

3.9

3.3

9.9

3.7

3.4

—

—

0.7

0.2

0.6

Communications Equipment Manufacturing

22.7

11.9

24.4

2.3

11.7

9.3

7.5

5.0

0.9

2.2

0.3

2.0

0.5

Fabric Mills

59.2

8.5

14.9

0.0

3.7

7.3

1.8

1.5

2.1

—

0.9

—

0.5

Household and Institutional Furniture and Kitchen
Cabinet Manufacturing

50.2

3.7

20.6

0.4

1.7

17.1

—

3.4

1.2

—

1.1

0.5

0.4

Engine, Turbine, and Power Transmission Equipment
Manufacturing

42.5

9.8

20.6

1.0

4.1

14.0

2.3

3.7

1.0

—

1.0

—

0.4

Industrial Machinery Manufacturing

42.5

9.8

20.5

1.0

4.1

14.0

2.3

3.7

1.0

—

1.0

—

0.4

Agriculture, Construction, and Mining Machinery
Manufacturing

42.6

9.8

20.5

1.0

4.0

14.0

2.3

3.7

1.0

—

1.0

—

0.4

Motor Vehicle Body and Trailer Manufacturing

38.3

10.9

18.7

0.6

5.0

12.5

4.9

4.0

0.8

1.6

1.4

1.2

0.4

Electrical Equipment Manufacturing

28.1

23.1

16.9

8.0

6.6

10.0

3.8

2.3

0.5

—

0.7

—

0.4

84
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (%)

Process
Heating (%)

53.1

Cement and Concrete Product Manufacturing

Process
Cooling And
Refrigeration(%)
Facility
Lighting (%)

Machine Drive(%)

Glass and Glass Product Manufacturing

Subsector

Other Process
Use (%)

Other Facility
Support (%)

Conventional
Boiler Use (%)

End Use Not
Reported (%)

OnsiteTransportation
(%)

1.0

4.1

14.1

2.3

3.7

1.0

—

1.0

—

0.4

24.2

0.7

7.1

14.0

1.9

4.9

1.6

—

0.6

0.2

0.3

Commercial and Service Industry Machinery
Manufacturing

42.5

9.8

20.6

1.0

4.1

14.0

2.3

3.7

1.0

—

1.0

—

0.3

Sugar and Confectionery Product Manufacturing

43.0

4.3

8.4

0.2

26.2

7.4

1.8

2.0

3.1

2.2

0.9

0.4

0.3

Computer and Peripheral Equipment Manufacturing

22.3

11.7

24.5

2.3

11.9

9.3

7.6

5.0

0.9

2.2

0.3

2.0

0.3

Lime and Gypsum Product Manufacturing

52.9

25.1

6.4

1.8

3.9

4.9

2.1

1.5

0.9

—

0.4

0.2

0.3

Textile and Fabric Finishing and Fabric Coating Mills

59.1

8.5

15.0

0.0

3.7

7.3

1.8

1.5

2.1

—

0.9

—

0.3

Ship and Boat Building

37.6

10.8

18.9

0.7

5.2

12.6

5.0

4.0

0.9

1.6

1.4

1.2

0.3

Other Textile Product Mills

52.5

7.0

12.9

0.3

6.6

10.5

0.4

2.4

4.9

—

2.5

—

0.3

Machine Shops; Turned Product; and Screw, Nut,and Bolt Manufacturing

43.8

15.1

15.8

4.0

3.3

10.0

3.7

3.4

—

—

0.7

0.2

0.2

Clay Product and Refractory Manufacturing

52.7

25.1

6.5

1.8

3.9

4.9

2.2

1.5

0.9

—

0.4

0.2

0.2

Spring and Wire Product Manufacturing

43.7

15.1

15.9

4.0

3.3

10.0

3.7

3.4

—

—

0.7

0.2

0.2

Other Transportation Equipment Manufacturing

38.3

10.9

18.7

0.6

5.0

12.5

4.9

4.0

0.8

1.6

1.4

1.2

0.2

Ventilation, Heating, Air-Conditioning, and Commercial
Refrigeration Equipment Manufacturing

42.2

9.8

20.7

1.0

4.1

14.1

2.4

3.7

1.0

—

1.0

—

0.2

Household Appliance Manufacturing

28.5

23.4

16.7

7.8

6.5

9.9

3.7

2.3

0.5

—

0.7

—

0.2

Manufacturing and Reproducing Magnetic and
Optical Media

23.0

12.0

24.3

2.3

11.6

9.3

7.4

4.9

0.9

2.2

0.3

1.9

0.1

Railroad Rolling Stock Manufacturing

37.9

10.8

18.8

0.7

5.1

12.6

5.0

4.0

0.8

1.6

1.4

1.2

0.1

Seafood Product Preparation and Packaging

42.8

4.3

8.4

0.2

26.4

7.4

1.8

2.0

3.1

2.2

0.9

0.4

0.1

Boiler, Tank, and Shipping Container Manufacturing

44.7

15.2

15.5

3.8

3.2

9.8

3.6

3.3

—

—

0.7

0.2

0.1

Office Furniture (including Fixtures) Manufacturing

49.9

3.7

20.8

0.4

1.7

17.2

—

3.5

1.2

—

1.1

0.5

0.1

Audio and Video Equipment Manufacturing

23.0

12.0

24.3

2.3

11.6

9.3

7.4

4.9

0.9

2.2

0.3

1.9

0.1

Tobacco Manufacturing

37.0

4.4

11.9

0.2

19.7

9.4

2.5

3.3

1.6

7.6

2.0

0.4

0.1

Electric Lighting Equipment Manufacturing

28.6

23.4

16.7

7.8

6.5

9.9

3.7

2.3

0.5

—

0.7

—

0.1

Cut and Sew Apparel Manufacturing

40.6

5.0

29.1

—

1.8

17.1

—

3.4

2.2

—

0.8

—

0.1

Sawmills and Wood Preservation

73.4

6.2

7.4

0.3

0.7

5.9

1.1

1.6

1.8

1.1

0.2

0.3

0.1

Cutlery and Handtool Manufacturing

43.9

15.1

15.8

4.0

3.3

9.9

3.7

3.4

—

—

0.7

0.2

0.1

Textile Furnishings Mills

52.2

6.9

13.0

0.3

6.6

10.6

0.4

2.5

4.9

—

2.5

—

0.0

Other Furniture Related Product Manufacturing

50.8

3.8

20.4

0.4

1.7

16.9

—

3.3

1.2

—

1.1

0.5

0.0

85
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (%)

ElectroChemicalProcesses (%)

20.6

11.7

Other
Nonprocess
Use (%)

Facility HVAC
(%)

9.8

33.2

Process
Cooling And
Refrigeration(%)
Facility
Lighting (%)

Process
Heating (%)

42.3

Medical Equipment and Supplies Manufacturing

Machine Drive(%)

Metalworking Machinery Manufacturing

Subsector

Facility HVAC(%)

ElectroChemicalProcesses (%)

Other Process
Use (%)

Other Facility
Support (%)

Conventional
Boiler Use (%)

End Use Not
Reported (%)

OnsiteTransportation
(%)

Other
Nonprocess
Use (%)

7.9

10.2

0.3

3.7

10.8

0.7

2.3

0.7

—

0.3

—

0.0

63.0

7.9

10.2

0.3

3.7

10.8

0.7

2.3

0.7

—

0.3

—

0.0

Leather and Hide Tanning and Finishing

63.0

7.9

10.2

0.3

3.7

10.8

0.7

2.3

0.7

—

0.3

—

0.0

Hardware Manufacturing

43.6

15.0

15.9

4.0

3.3

10.0

3.7

3.5

—

—

0.7

0.2

0.0

Apparel Accessories and Other Apparel Manufacturing

39.9

5.0

29.5

—

1.8

17.3

—

3.5

2.2

—

0.8

—

0.0

Apparel Knitting Mills

39.8

5.0

29.5

—

1.8

17.3

—

3.5

2.2

—

0.8

—

0.0

Total

50.3

12.0

9.2

7.6

7.1

6.3

2.2

1.9

1.6

1.2

0.5

0.3

100.0

86
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Total (%)

Process
Heating (%)

63.0

Footwear Manufacturing

Process
Cooling And
Refrigeration(%)
Facility
Lighting (%)

Machine Drive(%)

Other Leather and Allied Product Manufacturing

Subsector

Figure 25. Industrial electricity use diurnal patterns by season, modeled for the CONUS in 201287
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

4 Future Workdsgrid is a new model designed to provide a solid basis for exploring questions about future
electricity load and its relationship to grid operations. Because dsgrid is a new model, thisdocumentation represents the beginning of an investigation rather than the end of an analysis.
Three areas of work are planned for the near future.

4.1 Additional ComStock Calibration

The ComStock model, although built from relatively mature prototype building energy models,is still under development and has not yet been fully calibrated against a sufficiently large bodyof hourly (or finer) commercial building metered data. In the near future, the team plans to
conduct several rounds of comparing ComStock to metered data sets, making model adjustments
accordingly, and again measuring level of agreement. Of most interest is comparing the
ComStock load profile results to aggregated commercial sector utility consumption data.
Consolidated Edison in New York, for example, releases historical 8,760 load data for portionsof their service territory, and Commonwealth Edison releases significant customer data thathas been anonymized but provides rate classification, which allows for the determination ofcommercial class customers. Calibration to these detailed energy data will provide valuable
insights into ComStock’s temporal accuracy, from which we expect to derive schedulingadjustments.

4.2 EFS Scenarios

In parallel with the dsgrid development efforts described here, the EFS team has been developing
a set of electrification scenarios using the EnergyPATHWAYS model (Mai et al. 2018). As partof that process, some sector models described here provided inputs to EnergyPATHWAYS,
so that the initial conditions of these two modeling efforts have been somewhat harmonized,
as depicted on the left side of Figure 26. The next step of these efforts is shown on the right sideof Figure 26, where EnergyPATHWAYS outputs in the form of descriptions of future building,industrial, and transportation stocks are translated and fed into the various components of dsgrid.
Each component will then be run using these adjusted inputs to create annual snapshots of future
electricity demand under multiple scenarios.

Figure 26. Relationship between dsgrid and decadal-scale adoption models

88This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

In anticipation of taking this step, the dsgrid and EnergyPATHWAYS teams have coordinatedon (1) use of the LandScan population data sets for both current conditions and (2) a 2030
county-level projection. Population is a key determiner of the magnitude and geographic
dispersion of energy use both now and as we might project it into the future. Thus, standardizing
on finely resolved population data sets will help ensure between-model compatibility andrealistic downscaling. Also, the dsgrid team has met with the EnergyPATHWAYS team to startevaluating what kinds of mappings will need to be performed to turn their outputs into
dsgrid inputs.
In addition to translating EnergyPATHWAYS outputs into a form that is usable by the dsgrid
sector models, we will also need to develop appropriate methods for projecting our gap models
and residuals into the future, possibly adjusting them based on some EnergyPATHWAYS
outputs as well. We look forward to implementing and executing this capability to construct
the EFS electrification scenario snapshots in 2018–2019.

4.3 Power System Geography

Finally, the team will be mapping dsgrid data to the geography of U.S. power systems. Somestate-level regulatory and reporting requirements notwithstanding, electric utilities andaggregations thereof are not generally aligned with political boundaries. Many electric utilities
have service territories in more than one state; many geographic regions are served by multiple
utilities; and the largest wholesale power markets (e.g., PJM and MISO) cover many states. The
latter are typically broken down into regions, but even those are quite coarse and cross stateboundaries. Relevant to dsgrid, they often cover areas with significantly different weather.
For the purposes of this report, we have focused on describing load at the state level oraggregates thereof, because this generally allows for high-level calibration and regionaldescription; however, to apply this and future data sets to power system models, we will needto disaggregate and reaggregate the data based on the logic of electric utilities and transmission
nodes. The FERC hourly load data used in this work was derived from disaggregations ofregional load time series to transmission nodes that were created to support several ongoing
renewable integration studies being conducted by NREL. We plan to retrace this logic andthe geographic and electrical entities associated with it to assign dsgrid load timeseries totransmission nodes for the operational modeling portion of EFS.

89
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References

Akbari, H., J. Eto, S. Konopacki, A. Afzal, K. Heinemeier, and L. Rainer. 1993. “Integrated
Estimation of Commercial Sector End-Use Load Shapes and Energy Use Intensities in
the PGE Service Area.” Lawrence Berkeley Lab., CA (USA).
http://utilityscalesolar.lbl.gov/sites/all/files/lbnl-35372.pdf.
Alkadi, Nasr, Michael Starke, Ookie Ma, Sachin Nimbalkar, Daryl Cox, Kevin Dowling,
Brendon Johnson, and Saqib Khan. 2013. “Industrial Geospatial Analysis Tool for
Energy Evaluation--IGATE-E.” In Proceedings of the Thirty-Fifth Industrial Energy
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Appendix A. Residential Sector Model Details
A.1 Data Sources and Methodology Details

Detailed documentation of the ResStock methodology can be found in Wilson et al. (2017).
For reference, a summary of the major input data sources is shown in Table A-1.
Table A-1. List of Data Sources Used to Derive Input Probability Distributions for ResStock
Number of
Probability
Distributions

Full Reference“Residential Energy Consumption Survey (RECS), 2009 RECS Survey Data,” U.S. Energy
Information Administration, accessed in 2012,https://www.eia.gov/consumption/residential/data/2009/.

2,792

Used engineering experience or calibration because of a lack of data

1,235

Wanyu R. Chan, Jeffrey Joh, and Max H. Sherman, Air Leakage of US Homes: Regression
Analysis and Improvements from Retrofit (Technical Report LBNL-5966E) (Berkeley, CA:
Lawrence Berkeley National Laboratory, 2012, Eqn. 2 and Table 1).

1,050

Thomas P. Wenzel, Jonathan G. Koomey, Gregory J. Rosenquist, Maria C. Sanchez, andJames W. Hanford, Energy Data Sourcebook for the U.S. Residential Sector (Technical Report
LBNL-40297) (Berkeley, CA: Lawrence Berkeley National Laboratory, 1997).

760

American Community Survey: Five-Year Summary File,” U.S. Census Bureau, 2012 (fromNational Historical Geographic Information System, Minnesota Population Center, 2015).

432“New Construction Builder Practice Survey Data,” National Association of Home Builders, 1982,
1987

152

Used default values from Eric Wilson, Cheryn Engebrecht Metzger, Scott Horowitz, and Robert
Hendron, 2014 Building America House Simulation Protocols (Technical Report NREL/TP-550060988) (Golden, CO: National Renewable Energy Laboratory, 2014),
http://energy.gov/eere/buildings/downloads/building-america-2014-house-simulation-protocols.

96“New Construction Overview,” Home Innovation Research Labs 1999, 2007 (New Housing
Characteristics; Insulation; Sheathing—Wall),http://www.homeinnovation.com/trends_and_reports/data/new_construction.

56“Residential Building Stock Assessment: Single-Family Characteristics and Energy Use,”
Northwest Energy Efficiency Alliance, 2012.

52

Kenneth Labs, John Carmody, Raymond Sterling, Lester Shen, Yu Joe Huang, and Danny
Parker, Buildings Foundation Design Handbook (Technical Report ORNL/Sub/86-72143/l) (OakRidge, TN: 1988).

48

International Code Council, 2009 International Energy Conservation Code (Washington, D.C.:
2009).

40

Ronald L. Ritschard, James W. Hanford, and A. Osman Sezgen, Single Family Heating andCooling Requirements: Assumptions, Methods, and Summary Results (Technical Report LBL30377) (Berkeley, CA: Lawrence Berkeley National Laboratory, 1992).

36

R.G. Lucas and P.C. Cole, Impacts of the 2009 IECC for Residential Buildings at State Level(Technical Report PNNL-18545) (Richland, WA: Pacific Northwest National Laboratory, 2009)35“Building America Field Data Repository,” National Renewable Energy Laboratory, 2015.

7

a The table lists the data sources used to develop the statistical model of housing stock characteristics used forthis analysis.

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A.2 Additional Calibration Details

Figure A-1 and Figure A-2 illustrate preliminary progress on calibrating weather-dependent enduse load profiles (space heating and air conditioning), using 2012 Utility Load Research Data(ULRD) from ComEd. This initial calibration used simple multipliers on the heating and coolingend uses; ongoing work involves determining the most appropriate parameter changes to achievea similar result (e.g., thermostat setpoints, seasonal usage patterns, and number of homes using
air conditioning).

2012

w/o Electric Heat

Uncalibrated

Calibrated

Figure A-1. Comparison of modeled and measured daily kWh load for average single-family
homes in the ComEd service territory (2012 data; homes without electric space heating)105
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2012

w/ Electric Heat

Uncalibrated

Calibrated
Figure A-2. Comparison of modeled and measured daily kWh load for average single-family
homes in the ComEd service territory (2012 data; homes with electric space heating)

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Appendix B. Commercial Sector Model Details
B.1 ComStock Overview

The commercial building sector stock model (ComStock) is a highly granular, bottom-up modelthat uses multiple data sources, statistical sampling methods, and advanced building energy
simulations to estimate annual subhourly energy consumption of commercial buildings acrossthe United States. The methodology used to develop ComStock closely mirrors that of theresidential building stock model described by (Wilson et al. 2016).
The ComStock modeling process consists of four constituent parts: a database of realcommercial building characteristics, conditional probability tables synthesized from these data,
the Parametric Analysis Tool (PAT) used to translate probability tables to simulation inputs, andbuilding energy simulations created using OpenStudio and simulated with EnergyPlus. The
following sections build on the ComStock overview provided in Section 2.2.2, and describe ingreater detail the data, modeling approaches, and assumptions used to develop ComStock. A
flowchart of the ComStock modeling process is depicted in Figure B-1.

Figure B-1. Workflow used to characterize the U.S. commercial building stock and model energy
use with county-level granularity

B.2 Commercial Building Characteristics

The scope of the ComStock model encompasses commercial buildings that can be mapped to
the 16 DOE prototype buildings: full service restaurant, hospital, large hotel, midrise apartment,
high-rise apartment, outpatient, primary school, quick service restaurant, retail, secondary
school, small hotel, strip mall, warehouse, small office, medium office, and large office (Goel et
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al. 2014). In addition to Goel et al. (2014), Deru et al. (2011) provides additional background on
the initial reference building models that were subsequently developed into the prototype
buildings used for codes and standards analysis. Commercial buildings whose principle activities
do not fall into one of the prototype building categories are currently not included in the core
portion of ComStock. 55 They are instead captured by the dsgrid commercial gaps model.
Every aspect of a commercial building ultimately influences its energy consumption—often incomplex and interrelated ways—thus necessitating robust modeling engines seeded with accurate
building characteristics. The primary building characteristics needed to accurately model
commercial building energy consumption were determined based on previous modelingexperience, available data, and engineering judgement. Many building characteristics (e.g.,number of floors, HVAC system type, and total area) depend on other influential variables (e.g.,
location, building type, and vintage). The building characteristics, dependencies, and data
sources used to establish the ComStock database are shown in Table 5. The hierarchicaldependency of the probability tables used to determine building simulation inputs are illustrated
in Figure B-2. These dependencies are somewhat arbitrary (e.g., the number of floors in abuilding may be codependent on building location and building type). Further statistical analysis
is needed to verify the optimal inheritance structure of these dependencies and the effects ofthese assumptions. For building characteristics listed in Table 5 that are not included in the
Figure B-2 dependencies flowchart, parameters were assigned values based on established
EnergyPlus and OpenStudio defaults.

Figure B-2. Visualization of probability table heirarchy, sampled using a Latin hypercube sampling

Supermarkets are a building type present in the original reference buildings, but they dropped from the prototype
building set. They are currently in the process of being added back in as full-fledged prototype buildings, at which
point they will also be incorporated into ComStock.
55

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B.3 Commercial Building Stock Data Sources

Simulating end-use electrification in U.S. commercial buildings requires accurate high-resolution
data on the type, size, location, and characteristics of existing commercial buildings. For thesecharacteristics, we use a combination of the data contained within the U.S. Energy Information
Administration (EIA 2016a) Commercial Buildings Energy Consumption Survey (CBECS) and
CoStar (2017), a commercial real estate inventory with building point data.
2012 Commercial Buildings Energy Consumption Survey (CBECS)
The 2012 CBECS is a national building survey conducted by the EIA. The most recent CBECS
began data collection in April 2013 for reference year 2012. The resulting data include surveyed
information on energy-related building characteristics and energy usage details for buildings
over 1,000 square feet in which at least 50% of the floorspace is used for commercial purposes.
CBECS public-use microdata (EIA 2016a) consist of anonymized building details for 6,720
surveyed buildings. The smallest level of geographic resolution for these data is the census
division—a relatively large geographic area covering multiple states and climate regions (FigureB-3). Each individual survey response also includes a sample weight, which represents thenumber of buildings in the general commercial building population that the observation
represents.

Figure B-3. The four U.S. census regions and nine census divisions, used by CBECS 2012

The buildings surveyed for CBECS were selected from a sampling frame of commercial
buildings developed by EIA. This sampling frame consists of two parts: an area frame and alist frame. The area frame, which accounts for approximately 80% of the total frame, was firstcreated in 2003 and was constructed using multi-stage area probability sampling. To create theframe, trained field staff walked or drove through selected areas (often the size of census tracts
or groups of census tracts) and recorded information about every commercial building. The list
frame, which accounts for about 20% of the total frame, consists of five administrative lists ofgovernment buildings, schools, hospitals, airports, and other large buildings, namely:

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•

U.S. federal buildings that are at least 200,000 square feet from Federal Real Property
Council files obtained from the General Services Administration•

Four-year colleges and universities estimated to have at least 1,000,000 square feet offloorspace from the Integrated Postsecondary Education Data System•

Hospitals with at least 200,000 square feet of floorspace purchased from the IMS
Healthcare Market Index•

Airports from the Federal Aviation Administration (FAA) Forms 5010, Airport Master
Record file, and FAA enplanements data file•

Other large buildings over 200,000 square feet (e.g., hotels and offices) from theCommon Premises Location file from Dun & Bradstreet.

Both the area and list frames were updated for the 2012 CBECS. The final sampling frame wasstratified by building size and type; the number of sampled buildings within each subgroup was
calculated to minimize the variance of the estimated energy consumption. Finally, the buildings
to be sampled were chosen using a probability proportionate to size selection procedure.
For the selected buildings, the CBECS was administered in two phases. During the first stage, a
trained interviewer administered a set questionnaire to the building respondent using a computerassisted recorded interview tool, either in-person or by telephone. If the building respondent
could not provide sufficient data, or the provided data were deemed erroneous using EIA
regression models, a second phase of data collection was used. In this Follow-up Energy
Suppliers Survey, EIA contacted the individual energy supplier for the building to obtain usage
and expenditure data from the supplier’s records.
During the subsequent data processing stage, items with nonresponses were treated with a hotdeck imputation technique, meaning the response for that missing item was randomly chosenfrom a completed response of another similar building. Additional steps were taken to adhere toconfidentiality requirements by removing potentially identifying information from the public-use
microdata file. This included masking specific square footage details and binning numeric
responses for building characteristics such as floor-to-ceiling height and year of construction.
For a full list of the confidentiality and masking procedures used by the EIA, refer to the User’s
Guide to the 2012 CBECS Public Use Microdata File (EIA 2016b). For more information on themethodology used to produce the CBECS data, visit the CBECS website. 56
CoStar Realty Database
CoStar Group, Inc. is a commercial real estate information and marketing company. As one of its
primary product offerings, CoStar maintains a full inventory of building point data (aggregatedto the census block level for the purpose of this analysis) with information on building area andmarket subtype (CoStar 2017). In the CONUS, CoStar reports data for a total of 2,860,201
commercial buildings of 94 property subtypes, which is just over half the number of buildings
estimated by CBECS.

56

https://www.eia.gov/consumption/commercial/

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According to CoStar, a team of research associates maintains the properties in CoStar’s databasethrough canvassing (500,000 properties nationwide annually); phone calls (10,000 calls daily
to brokers, owners, developers, and real estate professionals); photographs (1 million taken
annually); and data exchange (5.1 million changes per day) (CoStar Group, Inc. 2017).
Because the CoStar database is proprietary, additional documentation on CoStar sampling
methodologies and data collection methods is difficult to ascertain. Public information on CoStar
property data can be found on their website. 57
Data Source Limitations
Due to the vast quantity and variation in U.S. commercial buildings, it is unsurprising that an
accurate, high-resolution database of building counts, sizes, and types is difficult to create andmaintain. The two nation-wide databases identified and described in this report—CBECS and
CoStar—each entail limitations and uncertainties whose potential impacts were considered in thedevelopment of the ComStock database.
CBECS Limitations
With regard to building characteristics, the primary limitations of the available CBECS data
are its coarse geographic resolution, limited number of sampled buildings, and inaccuracy of
respondent reports. Because the highest available resolution of the CBECS data is the census
division level, conducting analyses at the desired geospatial resolution for dsgrid (county) is
infeasible without additional data manipulation.
The low number of surveyed buildings (6,720 total) in the 2012 CBECS provides only a limited
measure of the expected variability and kurtosis in the commercial building stock. However,
CBECS does provide relative standard error calculations for each estimate in the CBECS
summary tables. CBECS uses the jackknife method for estimating these standard errors,
whereby replicate weights are repeatedly used to estimate the statistic of interest and calculate
the differences between these estimates and the full-sample estimate (EIA 2016a). Using theprovided relative standard errors, calculations derived from CBECS data can be evaluated todetermine the precision of the survey estimate relative to the true population value.
Another weakness of the CBECS method is the dependence on the knowledge and accuracy ofthe building respondents when questioned by EIA’s trained surveyors. As analyzed in an EIA
report that retrospectively evaluated the efficacy of the 2012 CBECS (EIA 2015b), square
footage estimates and Principle Building Activity (PBA) designations collected during thebuilding survey portion of CBECS were limited by the respondents’ knowledge.
CoStar Limitations
Inadequate information regarding error metrics, survey forms, and geospatial coverage represents
a fundamental limitation of CoStar data. Certain fields, such as square footage are more likely tobe accurate, as they are critical to the commercial success of the data set and company, whileother fields are not as obviously trustworthy. Additionally, the commercial nature of the data set
leads to questions regarding coverage of real estate assets unlikely to be sold, such as publicly
owned schools, hospital campuses, and federally owned properties. Although ascertaining the57

http://www.costar.com/

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accuracy of these entries in individual municipalities is relatively easy, testing the accuracy ofthese measures across the United States is significantly more challenging.

B.4 Model Coverage and Gaps

Only DOE prototype buildings can be modeled in ComStock, so building types in CBECS and
CoStar must be mapped to these 16 supported building types. The overall building count andfloor area in ComStock is determined by the CoStar to DOE prototype mapping. Those buildings
are then populated with additional characteristics based on the CBECS to DOE prototype
mapping. This core model coverage is shown at the center of Figure B-4 as the circle labeled
ComStock. The commercial building gap model consists of those CBECS building types that canbe mapped to CoStar but not to the DOE prototype buildings. This is represented as the lightgreen area surrounding ComStock. Energy represented in CBECS but not in ComStock andunmappable to CoStar is unmodeled but quantifiable; CoStar building types mapped to neither a
DOE prototype nor CBECS are unrepresented and their energy use is completely unknown tothis analysis. Those two slices of energy use are shown on the left and right sides of Figure B-4
respectively. Table B-1 and Table B-2 show the mapping between CoStar and DOE prototypes
and 2012 CBECS and DOE prototypes respectively, along with the total number of buildings ofthose types reported in the CONUS. Table B-3 lists those 2012 CBECS building types that do
not map to a DOE prototype building, along with their estimated electricity use and the CoStar
building types they map to, if applicable.

Figure B-4. Relationship between major data sources and commercial model coverage

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Table B-1. Mapping of CoStar Building Combo Codes and DOE Prototype Buildings
CoStar Combo Code

DOE Prototype
Building

CONUS CoStar
Building Count

Flex_Light Distribution

warehouse

14,259Flex_Light Manufacturing

warehouse

21,252

Flex_Showroom

strip_mall

6,205

Health Care_Assisted Living

midrise_apartment

11,013

Health Care_Congregate Senior Housing

midrise_apartment

1,055

Health Care_Continuing Care Retirement Community

midrise_apartment

1,140

Health Care_Hospital

hospital

4,700

Health Care_Rehabilitation Center

outpatient

2,872

Health Care_Skilled Nursing Facility

outpatient

6,009

Hospitality_Hotellarge_hotel

29,876

Hospitality_Hotel Casino

large_hotel

284

Hospitality_Motelsmall_hotel

23,072

Hospitality_Single Room Occupancy Hotelmidrise_apartment

1,611

Industrial_Distribution

warehouse

32,783

Industrial_Service

warehouse

44,851

Industrial_Showroom

warehouse

7,038

Industrial_Truck Terminal

warehouse

6,605

Industrial_Warehouse

warehouse

410,878

Multi-Family_Apartments

midrise_apartment

528,468

Multi-Family_Dormitory

midrise_apartment

3,424

Office_Industrial Live/Work Unit

medium_office

2,101

Office_Loft/Creative Space

midrise_apartment

8,617

Office_Medical

outpatient

102,312

Office_Office Live/Work Unit

small_office

10,315

Office_Office/Residential

small_office

20,747Retail_Bank

small_office

55,825Retail_Bar

full_service_restaurant

15,715Retail_Day Care Center

primary_school

17,046

Retail_Department Store

retail2,937

Retail_Fast Foodquick_service_restaurant

77,699Retail_Freestanding

retail435,819

Retail_Garden Center

retail

5,412

Retail_Restaurant

full_service_restaurant

115,881

Retail_Storefront

strip_mall

134,575

Retail_Storefront Retail/Office

strip_mall

70,242Retail_Storefront Retail/Residential

strip_mall

120,904

Specialty_Airplane Hangar

warehouse

2,899

Specialty_Post Office

strip_mall

19,897

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CoStar Combo Code

DOE Prototype
Building

CONUS CoStar
Building Count

Specialty_Schools

primary_school

12,642

Specialty_Self-Storage

warehouse

29,984

Specialty_Sorority/Fraternity House

midrise_apartment

Matched count529
2,419,493

Flex_R&D

no_match

9,699

Flex_Telecom Hotel/Data Hostingno_match

1,057

Multi-Family_Manufactured Housing/Mobile HomePark

no_match

14,135

Office_Telecom Hotel/Data Hostingno_match

976

Retail_Auto Dealershipno_match

43,599Retail_Auto Repair

no_match

100,053

Retail_Bowling Alley

no_match

2,392

Retail_Convenience Storeno_match

46,419Retail_Drug Storeno_match

15,895Retail_Funeral Home

no_match

5,892

Retail_Health Clubno_match

4,350

Retail_Movie Theatre

no_match

2,811

Retail_Service Station

no_match

62,194Retail_Supermarket

no_match

12,679Retail_Truck Stop

no_match

760

Retail_Veterinarian/Kennel

no_match

7,076

Specialty_Airport

no_match

410

Specialty_Auto Salvage Facility

no_match

3,012

Specialty_Car Wash

no_match

16,241

Specialty_Cement/Gravel Plant

no_match

2,126

Specialty_Cemetery/Mausoleum

no_match

570

Specialty_Chemical/Oil Refinery

no_match

1,107

Specialty_Contractor Storage Yard

no_match

3,244

Specialty_Correctional Facility

no_match

412

Specialty_Drive-in Movie

no_match

46

Specialty_Landfill

no_match

144

Specialty_Lodge/Meeting Hall

no_match

12,922

Specialty_Lumberyard

no_match

698

Specialty_Marina

no_match

2,134

Specialty_Movie/Radio/TV Studiono_match

959

Specialty_Parking Garage

no_match

3,753

Specialty_Parking Lot

no_match

3,422

Specialty_Police/Fire Station

no_match

2,626

Specialty_Public Libraryno_match

1,498

Specialty_Radio/TV Transmission Facilities

no_match

611

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CoStar Combo Code

DOE Prototype
Building

CONUS CoStar
Building Count

Specialty_Railroad Yard

no_match

250

Specialty_Recycling Center

no_match

414

Specialty_Religious Facility

no_match

37,475Specialty_Shelter

no_match

1,134

Specialty_Shipyard

no_match

283

Specialty_Trailer/Camper Park

no_match

2,830

Specialty_Water Retention Facility

no_match

59

Specialty_Water Treatment Facility

no_match

496

Specialty_Winery/Vineyard

no_match

1,429

Sports & Entertainment_Amusement Park

no_match

659

Sports & Entertainment_Baseball Field

no_match

525

Sports & Entertainment_Casino

no_match

280

Sports & Entertainment_Golf Course/Driving Range

no_match

3,973

Sports & Entertainment_Horse Stables

no_match

948

Sports & Entertainment_Race Trackno_match

401

Sports & Entertainment_Skating Rink

no_match

978Sports & Entertainment_Swimming Poolno_match

569

Sports & Entertainment_Theater/Concert Hall

no_match

2,083

No match count

440,708

Industrial_Food Processing

not_commercial

3,870

Industrial_Manufacturingnot_commercial

105,632

Industrial_Refrigeration/Cold Storage

not_commercial

1,928

Industrial_Telecom Hotel/Data Hosting

not_commercial

1,216

Land_Commercial

not_commercial

379

Land_Industrial

not_commercial

59

Land_Residential

not_commercial

23

Specialty_Utility Sub-Station

not_commercial

1,524

Not commercial count114,631

Total building count2,974,832

a CoStar Combo Codes are derived from all possible combinations of Primary and Secondary property
types within the CoStar data set.

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Table B-2. Mapping of CBECS Detailed Principal Building Activity (PBAPLUS) and DOE Prototype
Buildings, and Associated Building Counts
CBECS
PBAPLUSCodeCBECS Building Count(final weight for U.S.)a

PBAPLUS Description

DOE Prototype
Building

2

Admin Officemedium_office558,0623Banksmall_office

91,071

4

Government Officemedium_office

113,270

5

Medical Officesmall_office

50,4386

Mixed-use Officesmall_office

125,082

7

Other Officesmall_office

74,451

9

Distribution

warehouse

151,36110

Warehouse

warehouse

426,972

18

Diagnostic

outpatient

60,30019

Clinicoutpatient

86,85527

Collegesecondary_school

27,21528

Primary School

primary_school

189,038

29

High School

secondary_school

42,70830

Preschool

primary_school

67,72631

Other School

primary_school

61,97332

Fast Foodquick_service_restaurant

92,313

33

Restaurant

full_service_restaurant

178,595

35

Hospital

hospital

9,579

36

Nursing

midrise_apartment

29,53537

Dorm

midrise_apartment

24,64738

Hotellarge_hotel

29,98239

Motelsmall_hotel

60,91340

Other Lodging

small_hotel

12,84842

Retailretail336,273

43

Other Retail

strip_mall

58,82150

Strip Mall

strip_mall

162,687

53

Bar/Lounge

full_service_restaurant

71,364

Matched count3,194,076

8

Laboratory

no_match

15,505

11

Self-storage

no_match

208,836

12

Convenience Storeno_match

79,10813

Convenience + Gas

no_match

51,65214

Grocery Storeno_match

44,734

15

Other Food Sale

no_match

1,246

16

Fire/Police

no_match

68,90817

Other Safety

no_match

8,655

20

Cold Storage

no_match

8,499

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CBECSPBAPLUSCodeCBECS Building Count(final weight for U.S.)a

PBAPLUS Description

DOE Prototype
Building

21

Religious Worshipno_match

411,799

22Entertainment

no_match

51,18023

Library

no_match

23,77824

Recreation

no_match

100,363

25

Socialno_match

135,435

26

Other Assembly

no_match

41,25834

Other Food Service

no_match

37,43941

Vehicle Dealer

no_match

43,16744

Postalno_match

30,34345

Repairs

no_match

84,492

46

Vehicle Service

no_match

214,001

47

Vehicle Storage

no_match

176,142

48

Other Service

no_match

113,568

49

Otherno_match

109,260

51

Enclosed Mallno_match

1,379

52

Courthouse

no_match

6,278

No match count1

Vacant

2,067,022
not_commercial

296,041

Not commercial count296,041

Total building count5,557,138a Building counts are the total of the final weights associated with each instance of a CBECS PBAPLUS

code. These values represent the entire U.S. (as opposed to the CONUS), as disaggregation by state is
not feasible with CBECS data (whose highest level of granularity is division).

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Table B-3. Mapping of CBECS Detailed Principal Building Activity (PBAPLUS) and CoStar Combo
Codes for those PBAPLUS Categories without a DOE Prototype MatchCBECSPBAPLUSCode2012 CBECS Annual
Electricity Use(TWh)

PBAPLUSDescription

CoStar Combo Code

8

Laboratory

Flex: R&D

19.0

12

Convenience Store

Retail: Convenience Store

14.5

13

Convenience + Gas

Retail: Service Station

12.1

Retail: Truck Stop
14

Grocery Store

Retail: Supermarket

33.7

16

Fire/Police

Specialty: Police/Fire Station

6.8

17

Other Safety

Specialty: Correctional Facility

8.0

21

Religious Worship

Specialty: Religious Facility

23.8

22

Entertainment

Sports & Entertainment:
Theater/Concert Hall

20.2

23

Library

Specialty: Public Library

11.6

24

Recreation

Retail: Bowling Alley

24.8

Retail: Health ClubSports & Entertainment:
Skating Rink
Sports & Entertainment:
Swimming Pool41

Vehicle Dealer

Retail: Auto Dealership

7.9

46

Vehicle Service

Retail: Auto Repair

14.4

47

Vehicle Storage

Specialty: Auto Salvage
Facility

8.6

Specialty: Parking Garage
Gap Subtotal

205.4

1

Vacantno_match

7.7

11

Self-storage

no_match

5.7

15

Other Food Sale

no_match

0.7

20

Cold Storage

no_match

12.7

25

Socialno_match

9.7

26

Other Assembly

no_match

14.0

34

Other Food Service

no_match

5.0

44

Postalno_match

4.1

45

Repairs

no_match

3.7

48

Other Service

no_match

6.6

49

Other

no_match

36.7

51

Enclosed Mallno_match

14.0

52

Courthouse

no_match

6.7

Unmodeled Subtotal

127.3

Total: Gap and Unmodeled

332.7

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As shown in Table B-1, eight of the 102 combo codes defined by CoStar were determined not tobe commercial building types. Fifty-three of the building combo codes cannot be adequately
modeled using DOE prototype buildings. These unmatched building types account for 15%(440,708 buildings) of the total reported commercial buildings in the CONUS. Of the 53 CBECSPBAPLUS categories, 25 cannot be adequately modeled using DOE prototype buildings andone is not considered commercial. The unmatched building types account for 30% (1,596,473
buildings) of the statistically weighted CBECS commercial building estimate.
There is inherent uncertainty in the mappings defined in Table B-1 and Table B-2. For instance,
one school building may be more closely aligned with a secondary school while another moreclosely aligns with a primary school. The implications of this mapping uncertainty on the
ComStock modeling process differ for CoStar and CBECS data. Because CoStar data are used todefine modeled building types, the unmatched CoStar building types are used to determine theunmodeled subsectors within ComStock. Therefore, the gap model heavily depends on thismapping, and uncertainty propagation is a significant concern. Potential unknown errors
in CBECS building type mappings to prototype building types will lead to potential
misrepresentation of building characteristic distributions dependent on prototype building type.

B.5 Modeling Details

The dependencies defined in Table 5 are mapped to discrete conditional probability distributions
based on the sample-space segmentations shown in Figure B-2. The result is a flattened
definition of a 12-dimensional probabilistic sample space maintained in a predefined spreadsheet
interface. The calculation of the tabular values relies on several data sets stored and maintainedby geospatial data science research staff at NREL. The calculations are performed using several
Python libraries and PostGIS functionality contained in a PostgreSQL server also maintained by
the NREL Geospatial Data Science team.
The discretized sample space so created is sampled based on a predetermined sample density.
For the purpose of this analysis, a sample density of 350,000 simulations across the CONUS wasused, based on previous analysis performed by the NREL Residential Buildings group (Wilson etal. 2016). The sample density will be examined in more detail in future work, particularly todetermine key convergence criteria at different geo-temporal scales.

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Appendix C. Industrial Sector Model Details

The Industrial Geospatial Analysis Tool for Energy Evaluation (IGATE-E) is a model thatutilizes multiple data sources and statistical approaches to estimate the energy consumption of
manufacturing plants across the United States. Originally developed by Oak Ridge National
Laboratory in 2012, this tool has been used to investigate the potential for demand response and
CHP at the plant level (Alkadi et al. 2013; Bhandari et al. 2018). Because industrial processes
vary greatly, IGATE-E does not attempt direct simulation of loads, but rather compiles data frommultiple sources and applies statistical techniques to estimate energy consumption down to theend-use level. Currently, IGATE-E is built in MATLAB with plans to release a web-based
version in the future. IGATE-E’s user interface is shown in Figure C-1.

Figure C-1. IGATE-E Version 2.0

C.1 IGATE-E: Energy Consumption

The core functionality of IGATE-E involves estimating electricity and natural gas consumption
for each manufacturing plant in the United States. To accomplish this, IGATE-E performs
regression analyses of energy consumption versus number of employees using data from theIndustrial Assessment Centers (IAC) database. Regression results are developed for individual
industries based on their North American Industry Classification System (NAICS) or Standard
Industrial Classification (SIC) system code. These systems are used to classify businesses
according to their primary economic activity, specifically the type of product being produced.
Working within this framework, the basic premise of IGATE-E is that manufacturing facilities
producing similar products (as categorized by their NAICS/SIC code) will utilize similar
processes which ultimately have similar energy intensities (i.e., energy usage per productproduced). In this case, number of employees are used as a proxy for the product being

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produced. 58 Within the model, a linear regression analysis is conducted by industry for every
three- and four-digit NAICS/SIC code. 59 An example of these results is shown in Figure C-2 forthe animal food manufacturing industry (NAICS 3111).

Figure C-2. NAICS: 3111 (Animal Food Manufacturing) regression results

Although this approach is comprehensive, some limitations exist. First, the IAC Database only
contains data for small- and medium-sized plants, 60 limiting its relevance in industries where large
manufacturing plants play a major role (DOE 2017a). Another limitation of the IAC Database is
the age of its results, with assessment available back to 1981. Improvements in energy efficiency
and labor productivity over the last 35 years may limit the accuracy of IGATE-E in industries
where fewer recent assessments have been completed. For use in dsgrid, IGATE-E ignoresassessments which do not provide information on a plant’s NAICS code. As a result of thisrequirement, IGATE-E only uses assessment data from 2002 onward, limiting problems that could
have arisen from using older data. Additional data sources have been explored to supplement this
data; however, the proprietary/competitive nature of plant level information has made it difficult tofind additional data beyond what is available from the IAC.
Using these regression results, energy consumption is estimated for individual plants based on
employment information from the Manufacturers' News, Inc. (MNI) EZ Select database of
approximately 294,000 manufacturing plants. These initial estimates are compared to the EIA’s
Manufacturing Energy Consumption Survey (MECS) at the three-digit NAICS code level (Table
C-1).
IGATE-E originally utilized annual sales data for this purpose, however, inconsistencies in quality and thesporadic availability of this data limited its value.
59
Within IGATE-E, a requirement of five IAC assessments is specified to perform an acceptable regression. Whilemost 4-digit industries have enough data to meet this requirement, those that do not utilize three-digit regressions
instead.
60
Eligibility requirements to qualify for an IAC Assessment include energy bills between $100,000-/yr and
2,500,000/yr.
58

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Table C-1. Comparison of IGATE-E estimates to 2014 MECS (Initial)
2014
MECS
(GWh)

Difference(GWh)

Difference
Relative to
2014
MECS (%)

NAICS

Manufacturing Type

IGATE-E(GWh)

311

Food manufacturing

77,18676,701

485

0.6

312

Beverage and tobacco products

14,413

10,014

4,399

43.9

313

Textile mills

10,841

12,687-1,846-14.6

314

Textile product mills

19,013

2,748

16,265591.9

315Apparel manufacturing

2,118

803

1,315

163.8

316

Leather and allied products

2,000

316

1,684

532.9

321

Wood products

30,291

22,353

7,938

35.5

322

Paper manufacturing

36,49099,474-62,984

-63.3

323

Printing and related support

33,796

14,232

19,564137.5

324

Petroleum and coal products

10,00967,662-57,653

-85.2

325

Chemical manufacturing

73,449183,096-109,647

-59.9

326

Plastics and rubber products

68,452

55,967

12,48522.3

327

Nonmetallic mineral products

39,79937,841

1,958

5.2

331

Primary metal manufacturing

85,595

138,437-52,842

-38.2

332

Fabricated metal products

47,244

43,683

3,561

8.2

333

Machinery manufacturing

44,63723,75820,87987.9334

Computer and electronic products

24,38532,864-8,479-25.8

335

Electrical equipment andcomponents

14,22911,764

2,465

336

Transportation equipment

42,72945,584-2,855-6.3

337

Furniture and related products

9,155

5,169

3,986

77.1

339

Miscellaneous

39,298

8,863

30,435343.4

725,129

894,016-168,887

-18.9

Total

21.0

By comparing IGATE-E’s estimates to the 2014 MECS, multiple differences emerge. Industries
where consumption is significantly underestimated include:•322: Paper•

324: Petroleum and Coal Products•

325: Chemicals•

331: Primary Metals.

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In reviewing the MECS, these industries were found to have the highest energy intensities,
suggesting that the lack of regression data for large manufacturing plants may be limiting
IGATE-E’s accuracy in these cases. For industries where consumption is significantly
overestimated, discrepancies in the number of establishments considered by MECS compared to
IGATE-E may be the primary issue. 61
While additional research is being conducted to understand these differences, IGATE-E’s initial
estimates are adjusted by industry and census region to match the MECS. This is accomplishedby scaling individual plant estimates so that aggregate consumption estimates match those fromthe 2014 MECS (Figure C-3). The primary drawback to this is that errors in the MECS, areultimately reproduced by IGATE-E. Future efforts will focus on the further development of
IGATE-E’s optimization approach to avoid “over adjusting” the initial estimates.

Figure C-3. Comparison of IGATE-E estimates to 2014 MECS (optimized)

C.2 IGATE-E: Load Shapes

To estimate hourly annual load shapes, a regression analysis of peak electricity versus electricity
data from the IAC Database is conducted. Next, an average load factor is estimated for eachindividual industry based on the slope of these regressions.
𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 =1
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 ∗ 8760

These estimates are used along with energy consumption estimates to determine a plant’s peakelectricity demand. The resulting load factor estimates are shown in Figure C-4.

For example, while the MNI database and the U.S. Census 2015 Statistics of U.S. Businesses(https://www.census.gov/programs-surveys/susb.html) largely agree on the number of manufacturing
establishments, reporting 294,427 and 292,825 respectively; the 2014 MECS estimates 175,107 establishments.
61

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Figure C-4. IGATE-E load factor estimates by industry

To construct hourly annual load shapes, data from EPRI’s Load Shape Library is utilized. This
online repository provides daily load shapes by sector and end use for various scenarios (i.e.,
weekday versus weekend and peak season versus off-peak season). To estimate energy
consumption at the end-use level, data from the 2014 MECS is applied. Next, categories
included in the EPRI Load Shape Library are matched to the most appropriate MECS end-use
category (Table C-2).
Table C-2. End-Use and Load Shape Category Mapping
2014 MECS End-Use Category

EPRI Load Shape Library Category

Conventional Boiler Use

OtherProcess Heating

Process Heating

Process Cooling and Refrigeration

Other

Machine Drive

Machine Drives

Electro-Chemical Processes

Other

Other Process Use

Other

Facility HVAC

HVAC

Facility Lighting

Lighting

Other Facility Support

Other

Onsite Transportation

Other

Other Nonprocess Use

Other

End Use Not Reported

Other

124
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Finally, disaggregated load shapes are constructed for each industry based on end-use
consumption estimates from the 2014 MECS and load shapes from the EPRI Load Shape Library(Figure C-5). The resulting load shapes are either “stretched” or “flattened” on an industry-byindustry basis to match the load factor estimates derived previously. This approach is ultimately
limited by the granularity and accuracy of the IAC Database, the MECS and the EPRI Load
Shape Library.

Figure C-5. NAICS: 3111 (Animal Food Manufacturing) normalized load shape

Within IGATE-E, load shapes are applied at the individual plant-level based on a plant’s peakelectricity demand. For use in dsgrid, plant-level peak demand estimates are compiled by county
and NAICS code, with the resulting output being matched to the corresponding normalized load
shape (with adjustments made to account for time zones and daylight-saving time policies).

C.3 Industrial Gap Model

Within the industrial sector, IGATE-E’s methodology has specifically been developed for the
manufacturing subsector. Subsectors not covered by IGATE-E include agriculture, forestry,
fishing and hunting; mining, quarrying, and oil and gas extraction; and construction. In dsgrid,these non-manufacturing subsectors are represented in the industrial gap model using annual
electricity estimates from the AEO, with generic load shapes taken from the EPRI Load ShapeLibrary. To develop county level results for these subsectors, AEO national estimates are firstdisaggregated to the state level based on employment data from the U.S. Census Bureau’s
Statistics of U.S. Businesses, and then to the county level based on number of establishments
data from the U.S. Census Bureau’s County Business Patterns (CBP).

125
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Appendix D. Transportation Sector Model Details

While dsgrid’s general transportation focus is on electrification of on-road transportation, thisreport focuses on constructing a model of historical electricity demand for the year 2012. The
transportation sector accounted for less than 0.2% of total electricity consumption in 2012 (7TWh out of the total approximately 3,800 TWh consumed (see Figure 14) and vehicle
electrification was still minimal (there were only approximately 75,000 PEV on the road in the
country in 2012 [Cazzola et al. 2017]). Of the 7 TWh of electricity consumed for transportationin 2012, 6.6 TWh were used by intercity rail, transit rail, and commuter rail (EIA 2015a). Railtransportation is thus covered in dsgrid as a “gap”; this appendix describes the simple method
used to estimate hourly electricity consumption profiles for electric transit in each state in 2012.
Methodologically, this appendix describes a process to disaggregate annual propulsion electricenergy consumption estimates, obtained from the National Transit Database (NTD) (FTA 2017),
spatially and temporally to generate a 50 x 8,784 matrix containing hourly energy consumption
estimates for electric rail transit in every state in 2012.

D.1 The National Transit Database (NTD)

Formed in 1974, the National Transit Database (NTD) is a federal reporting program for transit
agencies receiving federal funding under Federal Transit Administration (FTA) programs 5307
(Urbanized Area Formula) and 5311 (Rural Area Formula) (FTA 2017). NTD contains data
related to the financial, operation, and condition of U.S. transit systems. It also requires monthly
operating and safety statistics reports from agencies that file as a “Full Reporter” (those
operating more than 30 vehicles). The FTA submits annual NTD reports summarizing all transit
services and safety data to Congress for review and use.
NTD data products include transit provider profiles, national transit summaries and trends, transit
data tables, monthly data tables, and historical timeseries. Transit data tables are the source fordata from the Annual Module – a comprehensive report outlining the operations of all transit
services in a given year. For 2012, the NTD contains an annual report from 67 electric transit
services covering 45 U.S. urban areas that were leveraged to produce the dsgrid energy
consumption estimates.
To generate hourly load profiles, three tables from the NTD 2012 Annual Module were used:•2012 Annual Databases Energy Consumption: Reports annual energy consumption(in kWh propulsion) for transit services.

•

2012 Annual Database Service: Reports annual operating hours and capacities (numberof rail cars) for different scenarios, namely “Weekday,” “Saturday,” and “Sunday”•

2012 Annual Database Agency UZAs: Transit agency-to-urbanized area (UZA)crosswalk.

D.2 Methods

The total annual energy consumption (from the 2012 Annual Databases Energy Consumption)
data by transit service reported by NTD was disaggregated temporally and spatially to produce
hourly energy consumption estimates for electric transit in every state.
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First, annual operating hours and capacities (from 2012 Annual Database Service) are used tocompute an hourly utilization factor table, which is then used as a proxy to temporally
disaggregate the annual energy consumption. An hourly utilization factor 𝑈𝑈𝑖𝑖𝑖𝑖 is defined for eachtransit service 𝑖𝑖 and hour 𝑗𝑗 based on the number of vehicles 𝑛𝑛𝑖𝑖𝑖𝑖 , operating for transit service 𝑖𝑖 inhour 𝑗𝑗, and on the number of operating minutes 𝑡𝑡𝑖𝑖𝑖𝑖 , within that hour (e.g., if service starts at 7:30
a.m., the corresponding 𝑡𝑡𝑖𝑖𝑖𝑖 for the hour going from 7 a.m. to 8 a.m. would be 30 minutes):
𝑈𝑈𝑖𝑖𝑖𝑖 =𝑛𝑛𝑖𝑖𝑖𝑖∙𝑗𝑗=8764

max 𝑛𝑛𝑖𝑖,𝑗𝑗 �𝑗𝑗=1

𝑡𝑡𝑖𝑖𝑖𝑖60

The utilization factor varies between 0 and 1 and it represents the relative transit service load ineach hour relative to the maximum service load conditions for that transit service (i.e., operating
at max capacity for the full hour). Under the maximum load condition, 𝑈𝑈 = 1. Under a half loadcondition (e.g., full capacity for half an hour and half capacity for full hour), 𝑈𝑈 = 0.5. For no load
𝑈𝑈 = 0. The resulting utilization matrix is a 67 x 8,784 matrix with each cell containing 𝑈𝑈𝑖𝑖,𝑗𝑗 where𝑖𝑖 is a unique transit service and 𝑗𝑗 is the hour of the year (Table D-1).
Table D-1. Sample Utilization Matrix

𝒔𝒔𝟏𝟏

1

2

3

𝑈𝑈1 1

𝑈𝑈1 2

𝑈𝑈1 3

⋯

𝑈𝑈1 ⋯

8,784
𝑈𝑈1 8784

𝒔𝒔𝟐𝟐

𝑈𝑈2 1

𝑈𝑈2 2

𝑈𝑈2 3

𝑈𝑈2 ⋯

𝑈𝑈2 8784

𝒔𝒔𝟔𝟔𝟔𝟔

𝑈𝑈67 1

𝑈𝑈67 2

𝑈𝑈67 3

𝑈𝑈67 ⋯

𝑈𝑈67 8784

𝑒𝑒𝑖𝑖 𝑗𝑗 =𝑈𝑈𝑖𝑖 𝑗𝑗
𝑗𝑗=8,784∑𝑗𝑗=1
𝑈𝑈𝑖𝑖 𝑗𝑗

⋮

𝑈𝑈⋮ 1

𝑈𝑈⋮ 2

𝑈𝑈⋮ 3

𝑈𝑈⋮ ⋯

𝑈𝑈⋮ 8764

Hourly energy consumption estimates 𝑒𝑒𝑖𝑖 𝑗𝑗 for each transit service 𝑖𝑖 and hour 𝑗𝑗 are then derived
from the annual energy consumption estimates 𝐸𝐸𝑖𝑖 for each transit service 𝑖𝑖 reported in the 2012
Annual Databases Energy Consumption as:∙ 𝐸𝐸𝑖𝑖

Second, hourly energy consumption estimates are attributed to the appropriate urbanized area
using the NTD’s transit agency-to-UZA crosswalk (2012 Annual Database Agency UZAs) andthen aggregated spatially by state for dsgrid reporting. The 67 transit services considered in the
NTD data set are located in 45 urban areas that belong to 30 states. Some urban areas, however,span multiple states, as illustrated in Figure D-6 for the Portland urban area. For those urban
areas that span multiple states, the energy consumption was allocated across states based on topdown annual electricity sales for the transportation sector (EIA 2013a).

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Figure D-6. The Portland, Oregon (red)-Washington (blue) urban area spans two states

Finally, all operating times were standardized to EST to sync electrical load profiles across all
U.S. time zones.

D.3 Results

The methodology described above is used to generate a 50 x 8,784 matrix containing hourly
timeseries of transit rail hourly electricity in kWh, one for each of the fifty states. Figure D-7
shows a map of the 2012 annual electricity demand for rail transport in each state. The
magnitude of electric rail transit service varies a lot across the country: high energy demand isconcentrated in large urban areas; many states have little to no electric rail transit.
To illustrate the hourly load profiles corresponding to rail transport, Figure D-8 shows the load
profiles over one sample week for three states: California (CA), New York (NY), and Florida(FL). Results show the significant differences between week-end and weekdays as well as theimpact of changes of service during each day (intra-hourly changes are not fully captured by the
NTD data set used for this analysis).

128
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Table D-1 shows the first 12 hours of the state-level energy estimates (in kWh) used in dsgrid.

Figure D-7. 2012 annual electricity demand for rail transport by state

Figure D-8. Sample electrical transit load profiles for California, New York, and Florida
Profiles are shown in local time to better compare the operational profiles of different states.

129
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Table D-1. Sample of Energy Estimation Matrix
State

1

2

3

4

5

6

7

8

9

10

11

12

AK

0

0

0

0

0

0

0

0

0

0

0

0

AL

0

0

0

0

0

0

0

0

0

0

0

0

AR

0

0

0

0

0

0

0

0

0

0

0

28

AZ

1,556

1,556

1,348

0

0

0

1,167

1,556

1,556

1,556

1,556

1,556

CA

43,02742,46041,48035,87419,0170

3,569

14,93625,19325,74425,74443,027

CO

3,109

3,109

3,109

3,109

3,109

3,109

3,109

3,109

3,109

3,109

3,109

3,109

CT

26,70926,70926,70919,142

17,806

26,70926,70926,70926,70926,70926,70926,709

DE

0

0

0

0

0

0

0

0

0

0

0

0

FL

0

0

0

0

0

5,431

5,431

5,431

5,431

5,431

5,431

5,431

GA

7,046

7,046

0

0

0

1,174

7,046

7,046

7,046

7,051

7,051

7,051

HI

0

0

0

0

0

0

0

0

0

0

0

0

IA

0

0

0

0

0

0

0

0

0

0

0

0

ID

0

0

0

0

0

0

0

0

0

0

0

0

IL

24,891

24,891

24,631

24,14824,14824,891

24,891

24,891

24,891

24,891

24,891

24,891

IN

1,389

1,389

555

0

0

926

1,389

1,389

1,389

1,389

1,389

1,389

KS

0

0

0

0

0

0

0

0

0

0

0

0

KY

0

0

0

0

0

0

0

0

0

0

0

0

LA

446

0

0

0

0

0

446

446

446

446

446

446

MA

19,594

4,089

0

0

0

3,607

19,594

19,594

19,594

19,594

19,594

19,594MD

31,3601,045

31,36031,36035,3138,134

35,83535,83535,83536,861

39,682

39,682

ME

0

0

0

0

0

0

0

0

0

0

0

0

MI

368

368

147

0

0

246

368

368

368

368

368

368

MN

1,697

1,697

1,697

1,697

1,697

1,697

1,697

1,697

1,697

1,697

1,697

1,697

MO

2,078

2,078

1,351

0

0

2,078

2,078

2,078

2,078

2,078

2,078

2,078

MS

0

0

0

0

0

0

0

0

0

0

0

39

MT

0

0

0

0

0

0

0

0

0

0

0

0

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State

1

2

3

4

5

6

7

8

9

10

11

12

NC

0

0

0

0

0

0

0

0

0

0

0

0

ND

0

0

0

0

0

0

0

0

0

0

0

0

NE

0

0

0

0

0

0

0

0

0

0

0

0

NH

0

0

0

0

0

0

0

0

0

0

0

0

NJ

29,15829,081

27,605

3,892

797

16,63029,15829,15829,15829,15829,15829,158

NM

0

0

0

0

0

0

0

0

0

0

0

0

NV

1,112

1,112

1,112

1,112

1,112

1,112

0

0

0

0

1,112

1,112

NY

177,970

177,970

177,970

170,402

169,067

177,970

177,970

177,970

178,586

178,586

178,586

178,473

OH

3,157

1,692

0

761

3,157

3,157

3,157

3,157

3,157

3,157

3,157

3,157

OK

0

0

0

0

0

0

0

0

0

0

0

0

OR

3,834

3,834

3,730

3,715

2,105

0

1,795

3,715

3,715

3,715

3,804

3,834

PA

17,850

6,498

2,230

2,230

8,955

19,122

19,28522,50624,103

24,10324,13024,130

RI

0

0

0

0

0

0

0

0

0

0

0

0

SC

0

0

0

0

0

0

163

336

336

336

336

336

SD

0

0

0

0

0

0

0

0

0

0

0

0

TN

0

0

0

0

0

0

0

0

0

17

17

171

TX

8,522

8,287

6,050

0

5,647

8,067

8,317

8,522

8,522

8,522

8,522

8,522

UT

1,797

0

0

0

0

0

0

0

0

0

0

3,594

VA

0

0

0

0

0

0

0

0

0

0

0

407

VT

0

0

0

0

0

0

0

0

0

0

0

0

WA

2,632

2,632

2,488

2,259

647

0

552

1,142

1,835

2,483

2,510

2,576

WI

3,391

3,391

3,391

1,130

0

0

3,391

3,391

3,391

3,391

3,391

3,456

WV

0

0

0

0

0

0

0

0

0

0

0

0

WY

0

0

0

0

0

0

0

0

0

0

0

0

131
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Appendix E. Model Coverage Analysis Details

dsgrid requires the harmonization of multiple independent sectoral models and the representationof unmodeled sectors to completely depict present and potential future national electricity load.
Accomplishing both requires both a calibration across sectoral models and a quantification of thecontribution of unmodeled subsectors to total electricity and energy consumption.
To help meet these requirements, a top-down analysis of energy and electricity consumption
was carried out across modeled and unmodeled subsectors. The results provide insights into thecoverage of the dsgrid sectoral models, quantifying the relative contribution that each sectoralmodel can be expected to make to load totals while identifying gaps needing to be represented
independently.

E.1 Method

Energy consumption estimates by subsector and end use were derived from the 2009 RECS (EIA
2013d), the 2012 CBECS (EIA 2016a), and 2014 Manufacturing Energy Consumption Survey(EIA 2017a). These sector-specific survey data were used to determine relative contributions ofindividual subsectors and end uses to total sectoral energy use, in terms of both electricity andoverall energy.
The absolute contribution of individual subsectors and end uses to national electricity and energy
consumption was subsequently determined by applying a calibration factor to each sector
survey’s energy use data, aligning each survey total with the AEO 2015 reported sectoral energy
consumption for 2012. Because the ratio of electricity demand to total energy use differed
between the sector surveys and their corresponding AEO sector values, a least-squares procedure
was applied to co-optimize the calibration factors for best-fit alignment with both sectoralelectrical demand and sectoral total energy consumption.
As the industrial survey data (MECS) only covered manufacturing subsectors, the calibration
factor to match to the AEO sector total was calculated with respect to only the manufacturingsubcomponent of the AEO industrial sector data. The AEO non-manufacturing subcomponent—
which was further broken down into agriculture, construction, and mining subsectors—wasadded directly to the final data set (i.e., with a calibration factor of 1.0). A similar approach wasapplied to directly add AEO 2015’s 2012 subsectoral transport data to the final data set, given
the absence of a transport sector survey.
Each subsector in the reference data set could then be tagged as either modeled (either in detailor with a coarse gap model) or unmodeled, yielding an estimate of the size and location of gaps
between the bottom-up model results and national totals. The list of modeled and unmodeled
subsectors is provided in Table E-1.

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Table E-1. Subsectors Categorized as Modeled in Detail, Sectoral Gap Model, or Unmodeled
Modeled Subsectors(Detailed)

Modeled Subsectors(Gap)

Unmodeled
Subsectors

Residential
Data source: RECS2009

Single Family Detached
Five+ Unit Apartment

Single Family AttachedTwo- to Four Unit
Apartment
Mobile Home—

Commercial
Data source: CBECS
2012

Administrative/Professi
onal OfficeBank/Other Financial
Bar/Pub/Lounge
Clinic/Other Outpatient
HealthCollege/University
Distribution/Shipping
CenterDormitory/Fraternity/So
rorityElementary/Middle
SchoolFast Food
Government OfficeHighschool
Hospital/Inpatient
HealthHotelMedical Office—Diagnostic
Medical Office—
NonDiagnostic
Mixed Use Office
Motel/Inn
Non-Refrigerated
WarehouseNursing Home/Assisted
LivingOther Classroom
Education
Other Lodging
Other Office
Other Retail
Preschool/Daycare
Restaurant/Cafeteria
Retail Store
Strip Shopping Mall

Convenience Store
Convenience Store with
Gas Station
Entertainment/Culture
Fire Station/Police
StationGrocery Store/FoodMarketLaboratory
Library
Other Public Order and
SafetyRecreation
Religious/Worship
Vehicle DealershipShowroom
Vehicle Service Repair
Shop
VehicleStorage/Maintenance

Courthouse/Probation
OfficeEnclosed Mall
Other
Other Food Sales
Other Food Service
Other Public Assembly
Other Service
Post Office/Postal
CenterRefrigerated
Warehouse
Repair Shop
Self-storage
Social/Meeting
VacantSector

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Modeled Subsectors(Detailed)

Modeled Subsectors(Gap)

Unmodeled
Subsectors

Industrial
Data Sources:
MECS 2014(manufacturing) + AEO
2015 (nonmanufacturing)Food
Beverage and Tobacco
Products
Textile Mills
Textile Product Mills
ApparelLeather and Allied
Products
Wood Products
Paper
Printing and Related
SupportPetroleum and Coal
Products
Chemicals
Plastics and Rubber
Products
Nonmetallic Mineral
Products
Primary Metals
Fabricated Metal
Products
MachineryComputer andElectronic ProductsElectrical Equip.,
Appliances, andComponents
Transportation
Equipment
Furniture and Related
Products
Miscellaneous

Agriculture
Construction
Mining-Transportation
Data source: AEO 2015

Bus Transportation
Commercial Light
TrucksFreight Trucks
Light-Duty Vehicles

Rail—Passenger

Air
Lubricants
Military Use
Natural Gas Pipeline
Rail—Freight
Recreational Boats
Shipping—Domestic
Shipping—International

Sector

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E.2 Results

Electricity Gaps
Eighty percent of 2012 national electricity consumption is represented in one of dsgrid’s four
core sectoral models. An additional 16% is represented via coarser gap models. Detailed model
gaps are split primarily between the commercial, residential, and industrial sectors, with only asmall share of unmodeled electrical load in the transportation sector (due to transportation’s
small contribution to overall load in 2012). These breakdowns are shown in Figure E-1.

Figure E-1. Proportion of annual electricity use in each sectoral modeling category

The two single-largest subsectors in terms of annual electrical load not captured by a detailed
core model are mobile homes (residential sector) and mining (industrial sector). Apartmentbuildings with 2–4 units (residential), single-family attached dwellings (residential) andconstruction (industry) were other non-core model subsectors each contributing more than 1% tototal national load. Though the commercial buildings sector accounts the largest amount of noncore-model load by sector, its gaps are spread out over a larger number of unmodeled orcoarsely-modeled subsectors. The largest non-core model subsectors by electrical load aresummarized in Table E-2 and Table E-3.

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Table E-2. Largest Subsectors Represented by Coarse Gap Models, by Total Electricity Use
SectorCommercial

Residential

Industrial

Transportation

Gap-Modeled
Subsector

Proportion of Sector
Electricity Use (%)

Proportion of National
Electricity Use (%)

Grocery Store /Food Market

2.7

1.0

Recreation

2.0

0.7

Religious Worship

1.9

0.7

Mobile Home

7.2

2.3

2–4 Unit Apartment

5.0

1.6

Single Family Attached

4.7

1.5

Mining6.5

2.1

Construction

4.8

1.6

Agriculture

2.7

0.9

Rail-Passenger

94.6

0.2

Table E-3. Largest Unmodeled Subsectors, by Total Electricity Use
SectorUnmodeled
Subsector

Proportion of Sector
Electricity Use (%)

Proportion of National
Electricity Use (%)

Commercial

Other

3.0

1.0

Other Public Assembly

1.1

0.4

Enclosed Mall

1.1

0.4

Total Site Energy Gaps
Seventy-six percent of 2012 total national energy use is nominally captured by the four core
sectoral models. Unlike with the electricity-only case, the largest modeling gaps are in transport
and industry, which have more than twice as much unmodeled or coarsely-modeled energy ascompared to the residential and commercial sectors. Figure E-2 provides a breakdown by sector
and model type.

136
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Figure E-2. Annual total site energy use gaps in sectoral models

The largest gap-model subsectors once again include mining, construction and 2–4 unit
apartments, with various transport subsectors (most notably air transport) becoming significant
gaps when consumption is analyzed in terms of all site energy. The largest non-core model
subsectors by total site energy are summarized in Table E-4 and Table E-5.

137
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Table E-4. Largest Subsectors Represented by Coarse Gap Models, by Total Site Energy Use
SectorUnmodeled Subsector

Commercial

Residential

Industrial

Transportation

Proportion of sector
site energy use (%)

Proportion of nationalsite energy use (%)

Religious Worship

2.5

0.3

Grocery Store/Food Market

2.3

0.3

Recreation

2.1

0.3

2–4 Unit Apartment

6.7

1.2

Single Family Attached

5.4

0.9

Mobile Home

4.6

0.8

Mining17.2

4.3

Construction

10.0

2.5

Agriculture

5.4

1.3

Passenger Rail

0.1

0.04

Table E-5. Largest Unmodeled Subsectors, by Total Site Energy Use
SectorUnmodeled Subsector

Commercial
Transportation

Proportion of sector
site energy use (%)

Proportion of nationalsite energy use (%)

Other

2.4

0.3

Other Public Assembly

1.2

0.2

Air

8.9

3.9

NG Pipelines

2.9

1.3

Military Transport

2.7

1.2

International Shipping

2.5

1.1

Freight Rail

1.7

0.7

Electric or Electrifiable Site Energy Coverage Analysis
The coverage analysis also categorized sector end uses by their viability for electrification toestimate overall electrification potential relative to model coverage. Because our categorization(Table E-6) is aligned with Table 1.1 in Mai et al. (2018), “electrifiable” in this instance does notcarry its vernacular meaning but is more a statement about whether electrification of the end use
or subsector is being considered by the wider Electrification Futures Study (EFS). Subsectors
without detailed survey data (transportation and non-manufacturing industrial) were treated as asingle end use.

138
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Table E-6. Electrifiable and Non-Electrifiable End Uses by Sector
SectorElectrifiable End Uses

End Uses Not Electrified
in the EFS

Commercial

Space Heating
Air Conditioning
Ventilation
Water Heating
Lighting
Cooking
Refrigeration
Office Equipment
Computing

Other

Residential

Space Heating
Air Conditioning
Water Heating
Refrigeration

Other

Industrial

Conventional Boiler Use
Process Heating
Process Cooling andRefrigeration
Machine Drive
Electro-Chemical Processes
Facility HVAC
Facility Lighting

End Use Not Reported
MiningAgriculture
Construction
Other Facility Support
Onsite Transportation
Other Process Use
Other Nonprocess Use

Transportation

Light-Duty Vehicles
Commercial Light Trucks
Freight Trucks
Bus Transportation

Rail – Freight
Rail – Passenger
Shipping – Domestic
Shipping – International
Air
Military Use
Recreational Boats
Lubricants
Natural Gas Pipelines

139
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We then summarize the proportion of site energy use that is either already electric, or is labeled
as “electrifiable” above, and compare that to the remaining energy, that is, non-electric siteenergy use in categories labeled as “end uses not electrified in the EFS.” Based on thesecategorizations, 82% of national end-use energy use was considered electric or electrifiable, with
91% of that energy represented by a detailed sector model, and 8% covered by a gap model.
Table E-7 provides a cross-tabulation across model fidelity and electrifiability.
Table E-7. End-Use Energy Share by Model Detail and Electrifiability
Electric or Electrifiable Site
Energy Use (%)

Non-Electric Energy whose
Electrification is Not Consideredin the EFS (%)

Detailed Model

74.2

2.1

Gap Model

6.1

7.3

Unmodeled

1.2

9.1

140
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Appendix F. Historical Year Model Results, CONUSLevel Description of Gap Models and Distributed
Generation

The residential gap model is summarized by subsector and end use in the body of the reportalongside the detailed residential modeling. The other gap models are characterized by reduced
resolution along at least one dimension. We summarize them here in the form of summary tables
and diurnal load shape plots.

F.1 Commercial Gaps

The commercial building gap model does not include end-use information. The load shape foreach subsector in the gap model follows the overall commercial building load shape at whateverfinest level resolution is available. As a result, at the CONUS level of aggregation it isapproximately equal to the top line load shape seen in Figure 24. Analogously, to the extent thatthe commercial gap model shape varies by region, the regional shapes are visible in thecommercial diurnal plots available by census division in Appendix G.
We summarize the commercial building gap model here in Table F-1 and Table F-2.
Table F-1. Commercial Gap Model Annual Summary for the CONUS in 2012,by Subsector and Census Division

Pacific

East
North
CentralWest
SouthCentral

2,474

9,889

2,810

3,789

8,356

2,634

876

1,417

1,209

33,452

Recreation (GWh)

4,535

4,215

6,204

1,849

1,691

1,444

2,009

918

1,882

24,746

Religious Worship
(GWh)

5,970

2,423

2,791

3,036

3,379

651

1,377

3,181

935

23,743Entertainment Culture
(GWh)

4,411

1,892

3,880

2,867

3,734

431

933

309

1,728

20,184

Laboratory (GWh)

5,460

3,052

3,135

807

2,626

2,123

-

135

1,630

18,968

Convenience Store
(GWh)

3,695

804

2,111

2,089

1,503

1,370

1,162

661

1,074

14,468

Vehicle Service
Repair Shop (GWh)

3,485

1,182

2,452

2,077

1,005

877

2,249

761

266

14,353

Convenience Storewith Gas Station
(GWh)

4,182

1,025

695

1,868

-

328

679

2,017

1,247

12,043Library (GWh)

1,841

501

1,231

5,432

680

1,130

129

552

122

11,619Vehicle Storage
Maintenance (GWh)

1,399

2,486

1,736

318

492

526

688

598

263

8,507

Other Public Orderand Safety (GWh)

2,252

474

1,013

2,973

554

88

245

283

121

8,003

Vehicle DealershipShowroom (GWh)

3,431

1,605

737

1,137

875

-

43

-

-

7,828

Fire Station/Police
Station (GWh)

2,050

526

544

1,086

297

1,049

845

369

-

6,768

Total (GWh)

45,18630,07429,34029,32825,191

12,650

11,235

11,202

10,476204,682

Census Division/
Subsector

South
Atlantic

Grocery Store/FoodMarket (GWh)Mid
New
Atlantic England

West
North
CentralEast
South
Central Mountain

141
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Total

Table F-2. Commercial Gap Model Annual Summary for the CONUS in 2012,
Proportions by Subsector and Census Division
CensusDivision/
Subsector

West
East
East
West
South
New
North South
North South Mid
Atlantic Pacific Central Central Atlantic England Central Central Mountain Total

GroceryStore/FoodMarket (%)

5.5

32.9

9.6

12.9

33.2

20.8

7.8

12.6

11.5

16.3

Recreation (%)

10.0

14.0

21.1

6.3

6.7

11.4

17.9

8.2

18.0

12.1

Religious
Worship (%)

13.2

8.1

9.5

10.4

13.4

5.1

12.3

28.4

8.9

11.6

Entertainment
Culture (%)

9.8

6.3

13.2

9.8

14.8

3.4

8.3

2.8

16.5

9.9

Laboratory (%)

12.1

10.1

10.7

2.8

10.4

16.8

-

1.2

15.6

9.3

Convenience
Store (%)

8.2

2.7

7.2

7.1

6.0

10.8

10.3

5.9

10.2

7.1

Vehicle Service
Repair Shop (%)

7.7

3.9

8.4

7.1

4.0

6.9

20.0

6.8

2.5

7.0

Convenience
Store with Gas
Station (%)

9.3

3.4

2.4

6.4

-

2.6

6.0

18.0

11.9

5.9

Library (%)

4.1

1.7

4.2

18.5

2.7

8.9

1.1

4.9

1.2

5.7

Vehicle Storage
Maintenance (%)

3.1

8.3

5.9

1.1

2.0

4.2

6.1

5.3

2.5

4.2

Other Public
Order and Safety
(%)

5.0

1.6

3.5

10.1

2.2

0.7

2.2

2.5

1.2

3.9

VehicleDealershipShowroom (%)

7.6

5.3

2.5

3.9

3.5

-

0.4

-

-

3.8

Fire
Station/Police
Station (%)

4.5

1.8

1.9

3.7

1.2

8.3

7.5

3.3

-

3.3

Total (%)

22.1

14.7

14.3

14.3

12.3

6.2

5.5

5.5

5.1

100.0

The non-building commercial gap models for municipal water and outdoor lighting aresummarized in Table F-3 and Table F-4 respectively. The corresponding load shapes for all of
the CONUS are depicted in Figure F-1 and Figure F-2.

142
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Table F-3. Municipal Water Gap Model Annual Summary for the CONUS in 2012, Electricity Use
and Proportions by Subsector and Census Division

Census Division

PublicWater
Supply(GWh)

Wastewater
Treatment
(GWh)

Total
(GWh)

PublicWater
Supply (%)

Wastewater
Treatment(%)

Total (%)

Pacific

15,827

4,637

20,465

77.3

22.7

26.2

South Atlantic

7,534

5,802

13,335

56.5

43.5

17.1

East North Central

5,836

4,495

10,331

56.5

43.5

13.2

Mid Atlantic

5,140

3,959

9,099

56.5

43.5

11.6

West South Central

4,576

3,524

8,100

56.5

43.5

10.4

Mountain

2,778

2,140

4,918

56.5

43.5

6.3

West North Central

2,579

1,986

4,565

56.5

43.5

5.8

East South Central

2,318

1,785

4,103

56.5

43.5

5.3

New England

1,817

1,400

3,217

56.5

43.5

4.1

Total

48,40529,726

78,131

62.0

38.0

100.0

Figure F-1. Municipal water gap model diurnal load shapes

143
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Table F-4. Outdoor Lighting Gap Model Annual Summary for the CONUS in 2012, Electricity Use
and Proportions by Subsector and Census Division
Census Division

Parking(GWh)

Roadway
(GWh)

Total
(GWh)

Parking (%)

Roadway(%)

Total (%)

South Atlantic

20,916

12,40633,322

62.8

37.2

19.5

Pacific

16,717

9,915

26,633

62.8

37.2

15.6

East North Central

16,2039,610

25,814

62.8

37.2

15.1

Mid Atlantic

14,271

8,465

22,736

62.8

37.2

13.3

West South Central

12,705

7,536

20,241

62.8

37.2

11.9

Mountain

7,713

4,575

12,288

62.8

37.2

7.2

West North Central

7,159

4,246

11,406

62.8

37.2

6.7

East South Central

6,435

3,817

10,252

62.8

37.2

6.0

New England

5,046

2,993

8,038

62.8

37.2

4.7

107,166

63,562

170,728

62.8

37.2

100.0

Total

Figure F-2. Outdoor lighting gap model diurnal load shapes for the CONUS in 2012

144
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F.2 Industrial Gaps

Similar to the commercial building gap model, the load shapes for the industrial gap model areessentially the same as the detailed modeling for the industrial sector taken as a whole, and noend-use specificity is provided. And, we summarize this model on an annual basis by census
division in Table F-5.

9,497

3,644

53,879

75.6

17.6

6.8

29.3

South Atlantic

7,229

12,226

7,833

27,288

26.5

44.8

28.7

14.8

Pacific

3,336

9,128

11,72024,183

13.8

37.7

48.5

13.1

Mountain

14,883

5,335

1,985

22,203

67.0

24.0

8.9

12.1

East North Central

3,941

7,975

2,565

14,481

27.2

55.1

17.7

7.9

Mid Atlantic

4,835

7,886

1,338

14,060

34.4

56.1

9.5

7.6

West North Central

5,220

4,730

2,193

12,144

43.0

39.0

18.1

6.6

East South Central

4,798

3,305

3,194

11,297

42.5

29.3

28.3

6.1

354

2,728

1,356

4,439

8.0

61.5

30.6

2.4

85,33562,81135,830183,975

46.4

34.1

19.5

100.0

New England
Total

Agriculture, Forestry,
Fishing and Hunting (%)

Agriculture, Forestry,
Fishing and Hunting (GWh)

40,738

Total
(GWh)

Construction (%)

Construction (GWh)

West South Central

Census Division

Mining, Quarrying, and Oiland Gas Extraction (%)

Mining, Quarrying, and Oiland Gas Extraction (GWh)

Table F-5. Industrial Gap Model Annual Summary for the CONUS in 2012, Electricity Use andProportions by Subsector and Census Division

Total
(%)

F.3 Transportation Gaps

The transportation gap model for electricity used for passenger rail is summarized in Table F-6.
The CONUS load shape is plotted in Figure F-3.

145
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Table F-6. Transportation Gap Model for Passenger Rail Electricity, Annual Summary for the
CONUS in 2012 by Census Division
Census Division

Total GWh)

Total (%)

Mid Atlantic

3,304

51.5

South Atlantic

793

12.4

New England

709

11.0

Pacific706

11.0

East North Central

623

9.7

Mountain124

1.9West South Central

114

1.8

West North Central

43

0.7

East South Central

2

0.0

6,417

100.0

Total

Figure F-3. Transportation gap model diurnal load shapes for the CONUS in 2012146
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F.4 Distributed Generation

The distributed generation model provides estimates of behind-the-meter generation from CHP
plants, other distributed thermal plants, and PV. This is done by sector and by state. The model issummarized for all of the CONUS in Table F-7. The diurnal generation patterns are shown in
Figure F-4. CHP and distributed thermal plants are modeled mostly as baseload plants. The solar
diurnal pattern is readily apparent in the DPV profiles.
Table F-7. Distributed Generation Model, Annual Summary for the CONUS in 2012
Sector/DG TypeCHP (GWh)

Industry

Commercial

Residential

Total

194,988

28,457375

223,820

Thermal DG (GWh)

6,782

561

—

7,343

Distributed PV
(GWh)

1,814

1,810

2,386

6,010

Total (GWh)

203,584

30,8282,761

237,173

CHP (%)

95.8

92.3

13.6

94.4

Thermal DG (%)

3.3

1.8

—

3.1

Distributed PV (%)

0.9

5.9

86.4

2.5

Total (%)

85.8

13.0

1.2

100.0

Figure F-4. Distributed generation model diurnal generation patterns for CONUS in 2012147
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Appendix G. Historical Year Model Results by
Census Division

We present results analogous to those in Section 3, but at a finer level of geographic detail,namely at the census division level. The census divisions are defined by the U.S. Census Bureau.
For the purposes of this report, we model only the contiguous United States. Those 48 states andthe District of Columbia (Washington, D.C.) are listed in Table G-1 next to their corresponding
census division and region. The other two states, Alaska and Hawaii, are both in the Pacificcensus division.
Table G-1. States Listed by Census Region and Division
Census Region

Census Division

StatesMidwestWest North Central

Iowa, Kansas, Minnesota, Missouri, Nebraska, North
Dakota, South Dakota

East North Central

Indiana, Illinois, Michigan, Ohio, Wisconsin

New England

Connecticut, Maine, Massachusetts, New Hampshire,
Rhode Island, Vermont

Mid Atlantic

New Jersey, New York, Pennsylvania

South Atlantic

Delaware, District of Columbia, Florida, Georgia,
Maryland, North Carolina, South Carolina, Virginia,
West Virginia

East South Central

Alabama, Kentucky, Mississippi, TennesseeWest South Central

Arkansas, Louisiana, Oklahoma, Texas

MountainArizona, Colorado, Idaho, New Mexico, Montana,Utah, Nevada, Wyoming

Pacific

California, Oregon, Washington

Northeast

South

West

148
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G.1 West North Central
Table G-2. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, West North Central Census Division
ComponentType

Component
Name

Top-down

Hourly load

360.6

DerivedT&D losses

21.3

Top-down

Annual energy

dsgridDistributed generation

dsgrid-core

Residential

Commercial

Industrial

Transport

Total

102.8

100.6

91.3

0.0

294.8

–

1.0

4.6

–

5.6

Gap models

11.8

27.2

12.1

0.0

51.2

dsgrid-core

Detailed sector models

83.7

65.2

69.3

–

218.2

DerivedTotal site energy

102.8

101.6

95.9

0.0

300.4

DerivedAnnual sector residuals

7.3

9.3

14.4

-0.0

31.0

DerivedHourly residuals

75.5

Figure G-1. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the West North Central census division in 2012

149
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Figure G-2. dsgrid hourly residuals shown in context for the West North Central census division.

Figure G-3. Historical and dsgrid load duration curves for the West North Central census divisionin 2012The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of thedetailed sector and gap models.

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Table G-3. Residential Subsectors, Summary of Electricity by End Use for the West North Central
Census Division in 2012
Interior
Space Interior Space
Water Exterior
Heat
Equipment Cooling Lights Heating Fans Systems Lights Pumps Rejection Total(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
Subsector
SingleFamilyDetached

32,662 14,179 10,298

Mobile
Home

2,008

717

416

364

SingleFamilyAttached

1,730

618

359

Apartment
in Building
2 to 4 Units

1,224

470

MidriseApartment
Building

347

143

Total

7,499

2,092

76

—

342

490

68

4

—

4,409

313

295

422

58

4

—

3,798

463

272

520

—

561

47

16

3,573

144

98

165

—

173

15

5

1,089

9,719 8,474

8,411

2,952

145

21 95,498

37,970 16,126 11,680

8,672 7,152

82,630

Table G-4. Residential Electricity Proportions by Subsector and End Use for the West North
Central Census Division in 2012

Subsector

Interior
Space Interior Space
Water Exterior
Heat
Equipment Cooling Lights Heating Fans Systems Lights Pumps Rejection Total
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)

Single Family
Detached

39.5

17.2

12.5

10.5

8.7

9.1

2.5

0.1

—

86.5

Mobile Home

45.5

16.3

9.4

8.2

7.8

11.1

1.5

0.1

—

4.6

Single Family
Attached

45.5

16.3

9.4

8.2

7.8

11.1

1.5

0.1

—

4.0

Apartment in
Building 2 to
4 Units

34.3

13.2

13.0

7.6 14.6

—

15.7

1.3

0.5

3.7

MidriseApartment
Building

31.9

13.1

13.2

9.0 15.1

—

15.8

1.3

0.4

1.1

Total

39.8

16.9

12.2

10.2

8.8

3.1

0.2

0.0 100.0

8.9

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Figure G-4. Residential electricity use diurnal patterns by season, modeled for the West North
Central census division 2012

152
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Table G-5. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the West North Central Census Division in 2012

Subsector

Interior
Space Exterior Space
Heat
Interior
Lights Equipment Fans Cooling Lights Heating Pumps Rejection(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
(GWh)

Total
(GWh)

Strip Mall

11,541

1,690

3,224

1,578

2,365

701

9

3

21,111

Large Office

4,116

4,746

3,346

2,436

826

114

393

134

16,111

Standalone
Retail Store

3,882

1,213

1,452

733

594

715

2

1

8,590

Medium Office

1,139

1,301

941

690

404

67

59

18

4,618

Warehouse

1,175

472

601

120

576

679

5

2

3,631

Small Office

829

1,139

575

382

323

128

5

3

3,385

Full Service
Restaurant

597

1,332

331

250

167

93

4

1

2,776

Large Hotel

368

613

405

286

99

71

10

3

1,855

Hospital

391

432

215

182

28

—

35

13

1,296

Primary School

319

252

152

102

40

72

7

3

947

Outpatient
Treatment
Facility

150

221

89

66

54

—

12

5

596

Small Hotel

52

75

9

6

36

8

1

0

187

Quick Service
Restaurant

6

35

12

7

3

1

0

0

64

13,522 11,352

6,838

5,516

2,650

541

186

65,168

Total

24,565

153
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-6. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the West North Central Census Division in 2012

Subsector

Space Exterior Space
Heat
Interior InteriorLights Equipment Fans Cooling Lights Heating Pumps Rejection
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)

Total
(%)

Strip Mall

54.7

8.0

15.3

7.5

11.2

3.3

0.0

0.0

32.4

Large Office

25.6

29.5

20.8

15.1

5.1

0.7

2.4

0.8

24.7

Standalone
Retail Store

45.2

14.1

16.9

8.5

6.9

8.3

0.0

0.0

13.2

Medium Office

24.7

28.2

20.4

14.9

8.7

1.4

1.3

0.4

7.1

Warehouse

32.4

13.0

16.5

3.3

15.9

18.7

0.1

0.0

5.6

Small Office

24.5

33.7

17.0

11.3

9.6

3.8

0.2

0.1

5.2

Full Service
Restaurant

21.5

48.0

11.9

9.0

6.0

3.4

0.2

0.1

4.3

Large Hotel

19.8

33.0

21.9

15.4

5.3

3.8

0.6

0.2

2.8

Hospital

30.2

33.4

16.6

14.0

2.2

—

2.7

1.0

2.0

Primary
School

33.7

26.6

16.1

10.8

4.2

7.6

0.7

0.3

1.5

Outpatient
Treatment
Facility

25.2

37.1

14.9

11.0

9.1

—

2.0

0.8

0.9

Small Hotel

27.7

40.3

4.8

3.0

19.4

4.4

0.3

0.1

0.3

Quick Service
Restaurant

9.2

54.4

18.2

11.1

5.4

1.7

0.0

0.0

0.1

Total

37.7

20.7

17.4

10.5

8.5

4.1

0.8

0.3

100.0

154
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-5. Commercial electricity use diurnal patterns by season, modeled for the West North
Central census division 2012

155
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

End Use Not Reported(GWh)

Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

Other Process Use(GWh)Electro Chemical
Processes (GWh)

Facility Lighting (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Subsector

Process Heating(GWh)

Machine Drive (GWh)

Table G-7. Industrial Manufacturing Subsectors, Summary of Model Results for the West North Central Census Division in 2012

Animal Slaughtering and Processing

2,652

263

1,611

516

454

14

111

123

189

136

57

26

6,151

Pesticide, Fertilizer, and Other Agricultural Chemical
Manufacturing

2,790

181

486

319

205

857

78

61

107

69

13

13

5,178

Plastics Product Manufacturing

1,957

586

292

370

346

—

—

83

18

—

16

—

3,668

Petroleum and Coal Products Manufacturing

2,688

113

140

126

84

48

24

33

25

37

0

3

3,321

859

1,103

47

200

140

734

50

38

15

63

5

9

3,263

Basic Chemical Manufacturing

1,720

111

290

192

124

511

46

36

64

41

8

8

3,150

Converted Paper Product Manufacturing

1,898

99

42

111

97

21

86

27

117

13

5

—

2,515

Iron and Steel Mills and Ferroalloy Manufacturing

Grain and Oilseed Milling
Pulp, Paper, and Paperboard Mills
Animal Food Manufacturing

961

95

577

185

163

5

40

44

68

49

20

9

2,217

1,599

83

35

93

81

17

71

23

97

11

4

—

2,115

892

89

548

175

154

5

38

42

64

46

19

9

2,081

Pharmaceutical and Medicine Manufacturing

1,008

65

172

113

73

304

27

22

38

25

5

5

1,856

Other Fabricated Metal Product Manufacturing

741

254

55

263

166

66

61

57

—

—

12

3

1,677

Aerospace Product and Parts Manufacturing

629

179

82

305

204

11

80

64

14

25

23

19

1,634

Architectural and Structural Metals Manufacturing

688

236

51

247

155

62

57

53

—

—

11

3

1,565

Dairy Product Manufacturing

668

66

397

128

113

3

27

30

47

34

14

6

1,534

Printing and Related Support Activities

837

64

116

263

131

18

22

44

13

—

17

—

1,526

Other Nonmetallic Mineral Product Manufacturing

797

378

58

97

74

26

32

23

13

—

6

2

1,506

Cement and Concrete Product Manufacturing

669

318

49

82

62

22

27

19

11

—

5

2

1,267

Foundries

298

381

16

69

48

251

17

13

5

22

2

3

1,125

Other Chemical Product and Preparation Manufacturing

561

36

95

63

40

167

15

12

21

14

3

2

1,029

Soap, Cleaning Compound, and Toilet Preparation
Manufacturing

521

34

90

59

38

159

14

11

20

13

2

2

965

Fruit and Vegetable Preserving and Specialty Food
Manufacturing

408

40

248

79

70

2

17

19

29

21

9

4

947

Other Food Manufacturing

400

40

239

77

68

2

17

18

28

20

8

4

921

Bakeries and Tortilla Manufacturing

369

37

222

71

63

2

15

17

26

19

8

4

853

Coating, Engraving, Heat Treating, and Allied Activities

351

121

27

127

80

32

30

28

—

—

6

2

803

156
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

End Use Not Reported(GWh)

Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

Other Process Use(GWh)Electro Chemical
Processes (GWh)

Facility Lighting (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Process Heating(GWh)

Machine Drive (GWh)

Subsector
Motor Vehicle Manufacturing

294

84

39

145

97

5

38

31

6

12

11

9

773

Motor Vehicle Parts Manufacturing

287

82

37

140

93

5

37

29

6

12

10

9

747

Nonferrous Metal (except Aluminum) Production andProcessing

196

250

10

44

31

160

11

8

3

14

1

2

731

Other General Purpose Machinery Manufacturing

308

71

30

151

103

7

17

27

7

—

7

—

729

Glass and Glass Product Manufacturing

385

182

28

46

35

13

15

11

6

—

3

1

724

Agriculture, Construction, and Mining Machinery
Manufacturing

297

69

28

143

98

7

16

26

7

—

7

—

697

Semiconductor and Other Electronic Component
Manufacturing

148

77

74

155

59

15

47

31

6

14

2

12

639

Other Wood Product Manufacturing

461

39

4

46

37

2

7

10

11

7

2

2

628

Resin, Synthetic Rubber, and Artificial Synthetic Fibers andFilaments Manufacturing

320

21

54

36

23

95

9

7

12

8

1

1

586

Paint, Coating, and Adhesive Manufacturing

308

20

53

35

23

94

9

7

12

8

1

1

570

Other Miscellaneous Manufacturing

171

60

37

126

73

4

10

25

8

—

3

1

519

Alumina and Aluminum Production and Processing

137

175

7

31

22

113

8

6

2

10

1

1

512

Rubber Product Manufacturing

259

78

39

49

46

—

—

11

2

—

2

—

486

Motor Vehicle Body and Trailer Manufacturing

182

52

24

89

60

3

23

19

4

7

7

6

477

Beverage Manufacturing

171

20

94

56

44

1

12

16

8

36

10

2

470

Other Electrical Equipment and Component Manufacturing

128

105

29

76

45

36

17

10

2

—

3

—

451

Navigational, Measuring, Electromedical, and Control
Instruments Manufacturing

102

53

52

109

42

10

33

22

4

10

1

9

449

Forging and Stamping

196

67

15

70

44

17

16

15

—

—

3

1

445

Household and Institutional Furniture and Kitchen Cabinet
Manufacturing

199

15

7

82

68

1

—

14

5

—

4

2

397

Other Transportation Equipment Manufacturing

136

39

18

67

45

2

17

14

3

6

5

4

356

Sugar and Confectionery Product Manufacturing

151

15

92

29

26

1

6

7

11

8

3

1

351

Industrial Machinery Manufacturing

147

34

14

71

48

3

8

13

3

—

3

—

345

Medical Equipment and Supplies Manufacturing

103

36

22

75

43

2

6

15

5

—

2

1

309

Metalworking Machinery Manufacturing

123

29

12

60

41

3

7

11

3

—

3

—

292

157
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

End Use Not Reported(GWh)

Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

Other Process Use(GWh)Electro Chemical
Processes (GWh)

Facility Lighting (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Process Heating(GWh)

Machine Drive (GWh)

Subsector
Machine Shops; Turned Product; and Screw, Nut, and Bolt
Manufacturing

122

42

9

44

28

11

10

10

—

—

2

1

279

Commercial and Service Industry Machinery Manufacturing

117

27

11

57

39

3

6

10

3

—

3

—

276

Steel Product Manufacturing from Purchased Steel

73

93

4

16

12

60

4

3

1

5

0

1

272

Communications Equipment Manufacturing

61

32

31

65

25

6

20

13

2

6

1

5

268

Engine, Turbine, and Power Transmission Equipment
Manufacturing

112

26

11

54

37

3

6

10

3

—

3

—

263

Lime and Gypsum Product Manufacturing

136

65

10

17

13

5

6

4

2

—

1

0

258

Electrical Equipment Manufacturing

71

58

17

43

25

20

9

6

1

—

2

—

253

Ventilation, Heating, Air-Conditioning, and Commercial
Refrigeration Equipment Manufacturing

76

18

7

37

25

2

4

7

2

—

2

—

180

Veneer, Plywood, and Engineered Wood Product
Manufacturing

131

11

1

13

10

1

2

3

3

2

0

1

177

Spring and Wire Product Manufacturing

77

26

6

28

17

7

6

6

—

—

1

0

175

Computer and Peripheral Equipment Manufacturing

29

15

15

32

12

3

10

7

1

3

0

3

130

Railroad Rolling Stock Manufacturing

48

14

6

24

16

1

6

5

1

2

2

2

127

Office Furniture (including Fixtures) Manufacturing

60

4

2

25

21

0

—

4

1

—

1

1

121

Other Textile Product Mills

61

8

8

15

12

0

1

3

6

—

3

—

115

Boiler, Tank, and Shipping Container Manufacturing

51

17

4

18

11

4

4

4

—

—

1

0

114

Household Appliance Manufacturing

30

25

7

18

10

8

4

2

0

—

1

—

105

Ship and Boat Building

38

11

5

19

13

1

5

4

1

2

1

1

100

Clay Product and Refractory Manufacturing

44

21

3

5

4

1

2

1

1

—

0

0

84

Seafood Product Preparation and Packaging

27

3

17

5

5

0

1

1

2

1

1

0

63

Cutlery and Handtool Manufacturing

27

9

2

10

6

2

2

2

—

—

0

0

62

Manufacturing and Reproducing Magnetic and Optical
Media

14

7

7

15

6

1

4

3

1

1

0

1

60

Fabric Mills

23

3

1

6

3

0

1

1

1

—

0

—

39

Other Leather and Allied Product Manufacturing

24

3

1

4

4

0

0

1

0

—

0

—

38

Audio and Video Equipment Manufacturing

8

4

4

8

3

1

2

2

0

1

0

1

33

Electric Lighting Equipment Manufacturing

9

7

2

5

3

2

1

1

0

—

0

—

31

158
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

End Use Not Reported(GWh)

Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

Other Process Use(GWh)Electro Chemical
Processes (GWh)

Facility Lighting (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Process Heating(GWh)

Machine Drive (GWh)

Subsector
Fiber, Yarn, and Thread Mills

13

2

1

3

2

0

0

0

0

—

0

—

21

Textile and Fabric Finishing and Fabric Coating Mills

11

2

1

3

1

0

0

0

0

—

0

—

18

Footwear Manufacturing

11

1

1

2

2

0

0

0

0

—

0

—

18

Cut and Sew Apparel Manufacturing

7

1

0

5

3

—

—

1

0

—

0

—

16

Leather and Hide Tanning and Finishing

9

1

1

1

2

0

0

0

0

—

0

—

15

Tobacco Manufacturing

5

1

3

2

1

0

0

0

0

1

0

0

14

Other Furniture Related Product Manufacturing

6

0

0

3

2

0

—

0

0

—

0

0

13

Sawmills and Wood Preservation

5

0

0

1

0

0

0

0

0

0

0

0

7

Textile Furnishings Mills

3

0

0

1

1

0

0

0

0

—

0

—

5

Hardware Manufacturing

2

1

0

1

0

0

0

0

—

—

0

0

4

Apparel Accessories and Other Apparel Manufacturing

0

0

0

0

0

—

—

0

0

—

0

—

0

33,716

7,201

7,007

6,863

4,852

4,080

1,492

1,440

1,201

831

400

222

69,305

Total

159
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-6. Industrial electricity use diurnal patterns by season, modeled for the West North
Central census division 2012

160
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-8. Distributed Generation Model, Annual Summary the West North Central Census
Division in 2012 by Sector and DG Type
Sector/DG TypeCHP (GWh)

Industry

Commercial

Residential

Total

4,512

938

—

5,450

Distributed PV (GWh)

47

46

29

121

Thermal DG (GWh)

6

7

—

13

Total (GWh)

4,565

991

29

5,585

CHP (%)

98.8

94.7

—

97.6

Distributed PV (%)

1.0

4.6

100.0

2.2

Thermal DG (%)

0.1

0.7

—

0.2

Total (%)

81.7

17.7

0.5

100.0

Figure G-7. Distributed Generation Model Diurnal Generation Patterns, West North Central census
division 2012

161
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G.2 East North Central
Table G-9. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, East North Central Census Division
ComponentType

Component
Name

Top-down

Hourly load

585.4

DerivedT&D losses

27.6

Top-down

Annual energy

dsgridResidential

Commercial

Industrial

Transport

Total

188.6

184.9

202.2

0.6

576.4

Distributed generation

0.0

4.8

17.1

–

22.0

dsgrid-core

Gap models

24.6

65.5

14.5

0.6

105.2

dsgrid-core

Detailed sector models

174.5

164.8

190.3

–

529.7

DerivedTotal site energy

188.7

189.7

219.4

0.6

598.4

DerivedAnnual sector residuals

-10.4

-40.6

14.5

-0.0

-36.4

DerivedHourly residuals

59.4

Figure G-8. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the East North Central census division in 2012162
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-9. dsgrid hourly residuals shown in context for the East North Central census division.

Figure G-10. Historical and dsgrid load duration curves for the East North Central census divisionin 2012The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of thedetailed sector and gap models.

163
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-10. Residential Subsectors, Summary of Electricity by End Use for the East North Central
Census Division in 2012
Interior
Space Interior Space
Water Exterior
Heat
Equipment Cooling Lights Heating Fans Systems Lights Pumps Rejection Total(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
Subsector
SingleFamilyDetached

70,344 24,364 22,774 20,152 15,792

Apartment
in Building
2 to 4 Units

4,013

1,169

1,649

SingleFamilyAttached

3,235

970

698

564

Mobile
Home

2,995

898

647

821

255

362

MidriseApartment
Building
Total

13,782

4,612

177

—

—

1,987

115

41 11,495

587

616

114

8

—

6,793

522

544

571

105

7

—

6,289

233

331

—

433

26

9

2,472

81,409 27,656 26,130 22,492 18,755

14,9697,252

333

50 199,046

1,020 1,501

171,997

Table G-11. Residential Electricity Proportions by Subsector and End Use for the East North
Central Census Division in 2012

Subsector

Interior
Space Interior Space
Water Exterior
Heat
Equipment Cooling Lights Heating Fans Systems Lights Pumps Rejection Total
(%)
(%)
(%)
(%)
(%)a
(%)
(%)
(%)
(%)
(%)

Single Family
Detached

40.9

14.2

13.2

11.7

9.2

8.0

2.7

0.1

—

86.4

Apartment in
Building 2 to
4 Units

34.9

10.2

14.3

8.9 13.1

—

17.3

1.0

0.4

5.8

Single Family
Attached

47.6

14.3

10.3

8.3

8.6

9.1

1.7

0.1

—

3.4

Mobile Home

47.6

14.3

10.3

8.3

8.6

9.1

1.7

0.1

—

3.2

MidriseApartment
Building

33.2

10.3

14.7

9.4 13.4

—

17.5

1.0

0.4

1.2

Total

40.9

13.9

13.1

11.3

7.5

3.6

0.2

0.0 100.0

9.4

164This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-11. Residential electricity use diurnal patterns by season, modeled for the East North
Central census division 2012

165
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-12. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the East North Central Census Division in 2012

Subsector

Interior
Space Exterior Space
Heat
Interior
Lights Equipment Fans Cooling Lights Heating Pumps Rejection(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
(GWh)

Total
(GWh)

Strip Mall

27,091

3,940

7,049

2,755

5,541

1,636

21

8

48,041

Large Office

12,175

13,949

9,235

6,076

2,466

408

916

350

45,574Standalone
Retail Store

11,202

3,460

3,748

1,495

1,702

1,769

11

4

23,390Medium Office

2,540

2,890

1,975

1,086

903

192

80

29

9,696

Warehouse

3,198

1,271

1,423

164

1,529

1,680

10

4

9,279

Small Office

2,194

3,002

1,429

774

857

318

7

5

8,585

Full Service
Restaurant

1,653

3,730

886

556

463

229

8

3

7,527

Large Hotel

952

1,530

862

607

233

105

44

18

4,350

1,048

1,475

581

493

81

—

82

32

3,793

Primary School

717

572

309

171

92

111

6

3

1,980

Outpatient
Treatment
Facility

554

818

218

109

198

—

21

9

1,927

Small Hotel

166

221

20

11

102

14

1

0

537

Quick Service
Restaurant

16

96

30

17

9

1

0

0

170

36,955 27,766

14,315

14,176

6,463

1,206

Hospital

Total

63,506463 164,850

166
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-13. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the East North Central Census Division in 2012

Subsector

Space Exterior Space
Heat
Interior InteriorLights Equipment Fans Cooling Lights Heating Pumps Rejection
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)

Total
(%)

Strip Mall

56.4

8.2

14.7

5.7

11.5

3.4

0.0

0.0

29.1

Large Office

26.7

30.6

20.3

13.3

5.4

0.9

2.0

0.8

27.6

Standalone
Retail Store

47.9

14.8

16.0

6.4

7.3

7.6

0.0

0.0

14.2

Medium Office

26.2

29.8

20.4

11.2

9.3

2.0

0.8

0.3

5.9

Warehouse

34.5

13.7

15.3

1.8

16.5

18.1

0.1

0.0

5.6

Small Office

25.6

35.0

16.6

9.0

10.0

3.7

0.1

0.1

5.2

Full Service
Restaurant

22.0

49.6

11.8

7.4

6.1

3.0

0.1

0.0

4.6

Large Hotel

21.9

35.2

19.8

13.9

5.4

2.4

1.0

0.4

2.6

Hospital

27.6

38.9

15.3

13.0

2.1

—

2.2

0.8

2.3

Primary
School

36.2

28.9

15.6

8.6

4.6

5.6

0.3

0.1

1.2

Outpatient
Treatment
Facility

28.7

42.5

11.3

5.7

10.3

—

1.1

0.5

1.2

Small Hotel

31.0

41.2

3.7

2.1

19.0

2.7

0.2

0.1

0.3

Quick Service
Restaurant

9.4

56.4

18.0

10.1

5.5

0.5

0.0

0.0

0.1

Total

38.5

22.4

16.8

8.7

8.6

3.9

0.7

0.3

100.0

167
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-12. Commercial electricity use diurnal patterns by season, modeled for the East North
Central census division 2012

168
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

4,544

Petroleum and Coal Products Manufacturing
Converted Paper Product ManufacturingPulp, Paper, and Paperboard Mills
Foundries

549

423

166

—

1,082

1,760

61

1,178

293

507

1,349

6,346

266

298

5,228

272

306

4,913

255

1,467

1,879

Pharmaceutical and Medicine Manufacturing

2,848

Other Fabricated Metal Product Manufacturing

2,081

Dairy Product Manufacturing
Animal Slaughtering and Processing

58

916

—

261

57

—

473

461

371

78

147

326

765

122

96

169

109

20

114

198

331

56

77

58

88

57

267

117

236

75

322

35

285

53

249

109

219

70

299

32

338

1,237

237

80

84

65

25

106

184

321

858

206

486

78

61

107

713

740

184

466

154

172

159

—

1,763

174

337

9

297

1,048

72

80

1,657

164

322

9

283

1,006

70

77

Resin, Synthetic Rubber, and Artificial Synthetic Fibers andFilaments Manufacturing

1,994

128

222

593

143

336

54

Other Nonmetallic Mineral Product Manufacturing

1,787

848

217

59

166

130

Motor Vehicle Manufacturing

1,281

366

632

22

422

Pesticide, Fertilizer, and Other Agricultural Chemical
Manufacturing

1,689

109

193

519

Coating, Engraving, Heat Treating, and Allied Activities

1,363

469

494

Architectural and Structural Metals Manufacturing

1,359

466

488

Other Chemical Product and Preparation Manufacturing

1,628

105

Nonferrous Metal (except Aluminum) Production andProcessing

793

Forging and Stamping

Total (GWh)

Basic Chemical Manufacturing

1,157

Other Nonprocess Use
(GWh)

1,030

694

Onsite Transportation
(GWh)

1,834

3,624

524

End Use Not Reported
(GWh)

6,129

Motor Vehicle Parts Manufacturing

1,548

Conventional Boiler
Use (GWh)

Plastics Product Manufacturing

8,105

Other Facility Support
(GWh)

2,205

Other Process Use(GWh)12,185

Process Cooling And
Refrigeration (GWh)

Facility HVAC (GWh)

9,491

Facility Lighting (GWh)

Process Heating(GWh)

Iron and Steel Mills and Ferroalloy Manufacturing

Subsector

Electro Chemical
Processes (GWh)

Machine Drive (GWh)

Table G-14. Industrial Manufacturing Subsectors, Summary of Model Results for the East North Central Census Division in 2012

97

36,04749—

11,484

132

112

9,429

20

8,320

1

8

7,841

14

—

6,928

13

—

6,499

9

15

5,541

69

13

13

5,244

—

33

10

4,711

123

89

37

17

4,046

118

85

36

16

3,843

42

74

48

9

9

3,652

72

51

30

—

13

5

3,377

171

167

134

28

53

48

41

3,364

124

294

47

37

65

42

8

8

3,134

124

311

104

115

107

—

—

22

6

3,116

122

307

102

113

105

—

—

22

6

3,090

182

486

117

276

44

34

61

39

7

7

2,987

1,009

178

646

125

42

44

34

13

55

5

8

2,951

1,291

442

461

115

290

96

107

99

—

—

20

6

2,928

Printing and Related Support Activities

1,468

113

462

31

230

203

39

76

23

—

30

—

2,675

Fruit and Vegetable Preserving and Specialty Food
Manufacturing

1,063

105

207

5

182

646

45

49

76

55

23

10

2,465

Bakeries and Tortilla Manufacturing

971

96

188

5

165

585

40

45

69

49

21

9

2,244

Other General Purpose Machinery Manufacturing

880

204

431

21

294

85

49

78

20

—

21

—

2,084

169
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

End Use Not Reported(GWh)

Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

Other Process Use(GWh)Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)

Facility HVAC (GWh)

Process Heating(GWh)

Machine Drive (GWh)

Subsector
Soap, Cleaning Compound, and Toilet Preparation
Manufacturing

1,111

72

126

340

81

193

31

24

43

27

5

5

2,058

Steel Product Manufacturing from Purchased Steel

548

697

123

448

87

29

30

23

9

38

3

5

2,042

Glass and Glass Product Manufacturing

1,060

502

127

35

97

76

42

30

17

—

7

3

1,997

Paint, Coating, and Adhesive Manufacturing

1,004

65

114

306

73

174

28

22

38

25

5

5

1,858

Other Food Manufacturing

767

76

148

4

130

459

32

35

54

39

16

7

1,766

Alumina and Aluminum Production and Processing

458

584

103

376

73

24

25

20

8

32

3

5

1,711

Engine, Turbine, and Power Transmission Equipment
Manufacturing

707

163

342

17

234

67

39

61

16

—

17

—

1,663

Metalworking Machinery Manufacturing

701

162

342

17

233

68

39

62

16

—

17

—

1,655

Rubber Product Manufacturing

837

251

159

—

148

126

—

36

8

—

7

—

1,571

Cement and Concrete Product Manufacturing

827

393

101

28

77

61

34

24

14

—

6

2

1,566

Grain and Oilseed Milling

672

66

129

3

114

403

28

31

47

34

14

6

1,549

Semiconductor and Other Electronic Component
Manufacturing

344

179

361

34

138

172

110

73

13

32

4

29

1,487

Other Wood Product Manufacturing

986

84

100

4

80

9

14

21

24

14

3

5

1,344

Animal Food Manufacturing

513

51

101

3

88

315

22

24

37

27

11

5

1,197

Motor Vehicle Body and Trailer Manufacturing

445

127

218

8

146

59

57

46

10

18

16

14

1,162

Other Electrical Equipment and Component Manufacturing

325

266

192

90

114

74

42

26

5

—

8

—

1,143

Machine Shops; Turned Product; and Screw, Nut, and Bolt
Manufacturing

499

172

180

45

113

38

42

39

—

—

8

2

1,138

Other Miscellaneous Manufacturing

355

125

261

8

150

77

20

53

17

—

6

3

1,076

Industrial Machinery Manufacturing

456

105

220

11

151

43

25

40

10

—

11

—

1,072

Navigational, Measuring, Electromedical, and Control
Instruments Manufacturing

220

115

235

22

89

112

72

48

9

21

3

19

965

Beverage Manufacturing

342

41

112

2

89

187

24

31

15

73

19

4

938

Other Transportation Equipment Manufacturing

352

100

172

6

115

46

45

36

8

14

13

11

919

Spring and Wire Product Manufacturing

375

129

136

34

85

28

32

29

—

—

6

2

856

Sugar and Confectionery Product Manufacturing

366

36

71

2

63

223

15

17

26

19

8

4

851

Aerospace Product and Parts Manufacturing

309

88

150

5

100

40

39

32

7

13

11

10

804

170
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

End Use Not Reported(GWh)

Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

Other Process Use(GWh)Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)

Facility HVAC (GWh)

Process Heating(GWh)

Machine Drive (GWh)

Subsector
Household and Institutional Furniture and Kitchen Cabinet
Manufacturing

396

29

163

3

135

13

—

27

10

—

9

4

789

Electrical Equipment Manufacturing

221

182

133

63

79

52

30

18

4

—

6

—

787

Commercial and Service Industry Machinery Manufacturing

275

63

133

7

91

26

15

24

6

—

6

—

646

Agriculture, Construction, and Mining Machinery
Manufacturing

267

62

129

6

88

25

15

23

6

—

6

—

627

Medical Equipment and Supplies Manufacturing

177

62

129

4

74

38

10

26

9

—

3

1

534

Veneer, Plywood, and Engineered Wood Product
Manufacturing

303

26

30

1

24

3

4

6

7

4

1

1

411

Ventilation, Heating, Air-Conditioning, and Commercial
Refrigeration Equipment Manufacturing

170

40

84

4

57

17

10

15

4

—

4

—

404

Boiler, Tank, and Shipping Container Manufacturing

175

60

61

15

38

12

14

13

—

—

3

1

392

Clay Product and Refractory Manufacturing

200

95

25

7

19

15

8

6

3

—

1

1

379

Office Furniture (including Fixtures) Manufacturing

185

14

77

1

64

6

—

13

5

—

4

2

371

Household Appliance Manufacturing

100

82

59

27

35

23

13

8

2

—

2

—

350

Lime and Gypsum Product Manufacturing

185

88

22

6

17

13

7

5

3

—

1

1

349

Cutlery and Handtool Manufacturing

91

31

33

8

21

7

8

7

—

—

1

0

208

Communications Equipment Manufacturing

46

24

50

5

19

24

15

10

2

5

1

4

204

Railroad Rolling Stock Manufacturing

77

22

38

1

26

10

10

8

2

3

3

2

203

Ship and Boat Building

76

22

38

1

26

10

10

8

2

3

3

2

203

Other Textile Product Mills

101

13

25

1

20

13

1

5

9

—

5

—

193

Electric Lighting Equipment Manufacturing

49

40

29

14

17

11

6

4

1

—

1

—

173

Fabric Mills

57

8

14

0

7

4

2

1

2

—

1

—

96

Computer and Peripheral Equipment Manufacturing

21

11

23

2

9

11

7

5

1

2

0

2

92

Audio and Video Equipment Manufacturing

18

9

19

2

7

9

6

4

1

2

0

1

77

Seafood Product Preparation and Packaging

31

3

6

0

5

19

1

1

2

2

1

0

71

Manufacturing and Reproducing Magnetic and Optical
Media

16

9

17

2

7

8

5

3

1

2

0

1

71

Textile and Fabric Finishing and Fabric Coating Mills

34

5

9

0

4

2

1

1

1

—

1

—

58

Other Leather and Allied Product Manufacturing

36

5

6

0

6

2

0

1

0

—

0

—

57

171
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

End Use Not Reported(GWh)

Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

Other Process Use(GWh)Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)

Facility HVAC (GWh)

Process Heating(GWh)

Machine Drive (GWh)

Subsector
Other Furniture Related Product Manufacturing

25

2

10

0

8

1

—

2

1

—

1

0

49

Fiber, Yarn, and Thread Mills

22

3

6

0

3

1

1

1

1

—

0

—

37

Sawmills and Wood Preservation

21

2

2

0

2

0

0

0

0

0

0

0

28

Cut and Sew Apparel Manufacturing

10

1

7

—

4

0

—

1

1

—

0

—

25

Footwear Manufacturing

11

1

2

0

2

1

0

0

0

—

0

—

18

Tobacco Manufacturing

6

1

2

0

2

3

0

1

0

1

0

0

16

Leather and Hide Tanning and Finishing

8

1

1

0

1

0

0

0

0

—

0

—

12

Textile Furnishings Mills

6

1

1

0

1

1

0

0

1

—

0

—

11

Hardware Manufacturing

2

1

1

0

0

0

0

0

—

—

0

0

5

—

1

Apparel Accessories and Other Apparel Manufacturing
Total

0

0

0

—

0

0

—

0

0

—

0

85,656

29,375

18,636

16,80913,003

12,565

4,141

3,839

2,517

2,247

954

172
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

593 190,334

Figure G-13. Industrial electricity use diurnal patterns by season, modeled for the East North
Central census division 2012173
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-15. Distributed Generation Model, Annual Summary the East North Central Census
Division in 2012 by Sector and DG Type
Sector/DG TypeCHP (GWh)

Industry

Commercial

Residential

Total

16,6584,757

021,416

Thermal DG (GWh)

439

32

—

471

Distributed PV (GWh)

39

38

21

97

17,135

4,828

21

21,985

CHP (%)

97.2

98.5

2.2

97.4

Thermal DG (%)

2.6

0.7

—

2.1

Distributed PV (%)

0.2

0.8

97.8

0.4

Total (%)

77.9

22.0

0.1

100.0

Total (GWh)

Figure G-14. Distributed Generation Model Diurnal Generation Patterns, East North Central
census division 2012174
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

G.3 New England
Table G-16. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, New England Census Division
ComponentType

Component
Name

Top-down

Hourly load

126.0

DerivedT&D losses

3.5

Top-down

Annual energy

47.2

45.4

27.8

0.6

121.0

dsgridDistributed generation

0.1

2.7

7.5

–

10.3

dsgrid-core

Gap models

8.0

23.9

4.4

0.7

37.1

dsgrid-core

Detailed sector models

42.7

58.7

30.4

–

131.8

DerivedTotal site energy

47.3

48.1

35.4

0.6

131.4

DerivedAnnual sector residuals

-3.4

-34.5

0.6

-0.1

-37.5

DerivedHourly residuals

Residential

Commercial

Industrial

Transport

Total

-36.6

Figure G-15. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the New England census division in 2012

175
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-16. dsgrid hourly residuals shown in context for the New England census division.

Figure G-17. Historical and dsgrid load duration curves for the New England census division in2012
The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of thedetailed sector and gap models.

176
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-17. Residential Subsectors, Summary of Electricity by End Use for the New England
Census Division in 2012
Interior Interior Space Water
Space Exterior
Heat
Equipment Lights Cooling Systems Fans Heating Lights Pumps Rejection Total(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
Subsector
SingleFamilyDetached

20,595

6,646

4,302

Apartment
in Building
2 to 4 Units

1,812

780

417

—

SingleFamilyAttached

1,115

232

200

MidriseApartment
Building

403

182

Mobile
Home

421

Total

24,3473,476 2,620

2,089

1,369

384

— 41,481

672

624

936

54

17

5,312

194

106

85

38

14

—

1,984

92

—

147

130

216

12

4

1,186

88

76

73

40

32

14

5

—

748

7,927

5,086

3,743 3,584

2,959

2,574

470

21 50,712

Table G-18. Residential Electricity Proportions by Subsector and End Use for the New England
Census Division in 2012

Subsector

Interior Interior Space Water
Space Exterior
Heat
Equipment Lights Cooling Systems Fans Heating Lights Pumps Rejection Total
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)

Single Family
Detached

49.6

16.0

10.4

8.4

6.3

5.0

3.3

0.9

—

81.8

Apartment in
Building 2 to
4 Units

34.1

14.7

7.8

— 12.6

11.7

17.6

1.0

0.3

10.5

Single Family
Attached

56.2

11.7

10.1

9.8

5.3

4.3

1.9

0.7

—

3.9

MidriseApartment
Building

34.0

15.3

7.8

— 12.4

10.9

18.2

1.0

0.3

2.3

Mobile Home

56.2

11.7

10.1

9.8

5.3

4.3

1.9

0.7

—

1.5

Total

48.0

15.6

10.0

7.4

7.1

5.8

5.1

0.9

0.0 100.0

177
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-18. Residential electricity use diurnal patterns by season, modeled for the New Englandcensus division 2012178
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-19. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the New England Census Division in 2012

Subsector

Interior
Exterior Space Space
Heat
Interior
Lights Equipment Fans Lights Cooling Heating Pumps Rejection(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
(GWh)

Total
(GWh)

Large Office

5,538

6,426

4,104

1,108

2,274

134

362

126

20,070

Strip Mall

8,646

1,265

2,163

1,771

603

546

5

2

15,000Standalone
Retail Store

3,729

1,190

1,051

567

334

602

4

1

7,478

Medium Office

1,401

1,598

1,026

499

497

87

43

14

5,164

Small Office

798

1,100

492

312

228

112

3

2

3,047

Warehouse

808

313

293

383

27

418

2

1

2,244

Full Service
Restaurant

474

1,063

252

132

113

68

2

1

2,105

Large Hotel

312

502

287

77

175

35

15

5

1,407

Hospital

261

372

191

20

116

—

23

9

992

Outpatient
Treatment
Facility

174

256

81

62

40

—

8

3

624

Primary School

154

123

82

21

35

12

2

1

429

Small Hotel

49

72

4

36

2

4

0

0

168

Quick Service
Restaurant

2

11

4

1

1

0

—

—

19

14,290 10,029

4,989

4,445

2,017

468

163

58,748

Total

22,347

179
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-20. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the New England Census Division in 2012

Subsector

Interior
Exterior Space Space
Heat
Interior
Lights Equipment Fans Lights Cooling Heating Pumps Rejection
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)

Total
(%)

Large Office

27.6

32.0

20.4

5.5

11.3

0.7

1.8

0.6

34.2

Strip Mall

57.6

8.4

14.4

11.8

4.0

3.6

0.0

0.0

25.5

Standalone
Retail Store

49.9

15.9

14.1

7.6

4.5

8.1

0.0

0.0

12.7

Medium Office

27.1

30.9

19.9

9.7

9.6

1.7

0.8

0.3

8.8

Small Office

26.2

36.1

16.1

10.2

7.5

3.7

0.1

0.1

5.2

Warehouse

36.0

14.0

13.0

17.1

1.2

18.6

0.1

0.0

3.8

Full Service
Restaurant

22.5

50.5

11.9

6.3

5.4

3.2

0.1

0.0

3.6

Large Hotel

22.2

35.7

20.4

5.5

12.4

2.5

1.0

0.3

2.4

Hospital

26.3

37.5

19.3

2.0

11.7

—

2.3

0.9

1.7

Outpatient
Treatment
Facility

28.0

41.1

12.9

9.9

6.4

—

1.3

0.5

1.1

Primary School

35.9

28.6

19.1

4.9

8.1

2.7

0.5

0.2

0.7

Small Hotel

28.9

43.0

2.5

21.6

1.3

2.4

0.2

0.1

0.3

Quick Service
Restaurant

9.4

55.5

19.4

5.6

7.6

2.4

—

—

0.0

Total

38.0

24.3

17.1

8.5

7.6

3.4

0.8

0.3

100.0

180
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-19. Commercial electricity use diurnal patterns by season, modeled for the New Englandcensus division 2012

181
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Pharmaceutical and Medicine Manufacturing

2,044

230

132

Pulp, Paper, and Paperboard Mills

349

Total (GWh)Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

End Use Not Reported(GWh)

Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

Other Process Use(GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)

Process Cooling And
Refrigeration (GWh)

Process Heating(GWh)

Subsector

Facility HVAC (GWh)

Machine Drive (GWh)

Table G-21. Industrial Manufacturing Subsectors, Summary of Model Results for the New England Census Division in 2012

615

148

56

44

77

50

9

9

3,763

1,458

85

76

32

16

74

65

21

89

10

4

—

1,929

Basic Chemical Manufacturing

830

92

53

140

246

60

22

17

31

20

4

4

1,519

Plastics Product Manufacturing

760

143

227

114

—

134

—

32

7

—

6

—

1,424

Converted Paper Product Manufacturing

1,029

60

54

23

11

53

46

15

63

7

3

—

1,363

Other Chemical Product and Preparation Manufacturing

626

70

40

106

187

45

17

13

23

15

3

3

1,149

Semiconductor and Other Electronic Component
Manufacturing

245

257

127

122

24

98

78

52

9

23

3

20

1,059

Other Nonmetallic Mineral Product Manufacturing

543

66

258

39

18

50

22

16

9

—

4

2

1,026

Resin, Synthetic Rubber, and Artificial Synthetic Fibers andFilaments Manufacturing

542

60

35

91

161

39

15

11

20

13

2

2

993

Navigational, Measuring, Electromedical, and Control
Instruments Manufacturing

192

205

100

98

19

78

63

42

7

19

2

16

843

Aerospace Product and Parts Manufacturing

322

156

92

42

5

105

41

33

7

13

12

10

838

Soap, Cleaning Compound, and Toilet Preparation
Manufacturing

355

40

23

62

109

26

10

8

14

9

2

2

658

Printing and Related Support Activities

351

110

27

48

7

55

9

18

5

—

7

—

639

Nonferrous Metal (except Aluminum) Production andProcessing

168

38

214

9

137

27

9

7

3

12

1

2

625

Paint, Coating, and Adhesive Manufacturing

333

38

22

58

102

24

9

7

13

8

2

2

617

Pesticide, Fertilizer, and Other Agricultural Chemical
Manufacturing

330

38

21

58

101

24

9

7

13

8

2

2

613

Petroleum and Coal Products Manufacturing

486

23

20

25

9

15

4

6

4

7

0

1

600

Other Wood Product Manufacturing

437

44

37

4

2

35

6

9

10

6

1

2

596

Other Electrical Equipment and Component Manufacturing

169

100

139

39

47

59

22

14

3

—

4

—

594

Other Miscellaneous Manufacturing

158

116

56

34

3

67

9

23

8

—

3

1

478

Beverage Manufacturing

173

57

21

95

1

45

12

16

8

37

10

2

476

Iron and Steel Mills and Ferroalloy Manufacturing

121

28

155

7

103

20

7

5

2

9

1

1

459

Glass and Glass Product Manufacturing

243

29

115

17

8

22

10

7

4

—

2

1

458

182
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

End Use Not Reported(GWh)

Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

Other Process Use(GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)

Process Cooling And
Refrigeration (GWh)

Process Heating(GWh)

Facility HVAC (GWh)

Machine Drive (GWh)

Subsector
Dairy Product Manufacturing

191

37

19

113

1

32

8

9

13

10

4

2

438

Fabric Mills

236

60

34

15

0

29

7

6

8

—

3

—

399

Other Fabricated Metal Product Manufacturing

176

63

60

13

16

39

15

13

—

—

3

1

399

Ship and Boat Building

133

67

38

18

2

45

18

14

3

6

5

4

353

Architectural and Structural Metals Manufacturing

142

51

49

11

13

32

12

11

—

—

2

1

322

Cement and Concrete Product Manufacturing

168

20

80

12

6

16

7

5

3

—

1

1

318

Coating, Engraving, Heat Treating, and Allied Activities

137

50

47

10

12

31

12

11

—

—

2

1

313

Bakeries and Tortilla Manufacturing

119

23

12

72

1

20

5

5

8

6

3

1

275

Medical Equipment and Supplies Manufacturing

89

65

31

19

2

37

5

13

4

—

2

1

268

Other General Purpose Machinery Manufacturing

112

55

26

11

3

37

6

10

3

—

3

—

265

Foundries

67

15

86

4

57

11

4

3

1

5

0

1

253

Fruit and Vegetable Preserving and Specialty Food
Manufacturing

102

20

10

62

1

17

4

5

7

5

2

1

237

Communications Equipment Manufacturing

50

53

26

26

5

20

16

11

2

5

1

4

219

Industrial Machinery Manufacturing

90

44

21

9

2

30

5

8

2

—

2

—

212

Fiber, Yarn, and Thread Mills

123

31

18

8

0

15

4

3

4

—

2

—

208

Textile and Fabric Finishing and Fabric Coating Mills

123

31

18

8

0

15

4

3

4

—

2

—

208

Commercial and Service Industry Machinery Manufacturing

87

42

20

8

2

29

5

8

2

—

2

—

205

Forging and Stamping

86

31

29

6

8

19

7

7

—

—

1

0

194

Computer and Peripheral Equipment Manufacturing

42

46

22

22

4

18

14

10

2

4

1

4

189

Steel Product Manufacturing from Purchased Steel

50

11

64

3

41

8

3

2

1

4

0

0

186

Metalworking Machinery Manufacturing

68

33

16

7

2

23

4

6

2

—

2

—

162

Electrical Equipment Manufacturing

40

24

33

9

11

14

5

3

1

—

1

—

143

Animal Slaughtering and Processing

61

12

6

37

0

10

3

3

4

3

1

1

140

Other Food Manufacturing

58

11

6

34

0

10

2

3

4

3

1

1

132

Motor Vehicle Parts Manufacturing

49

24

14

6

1

16

6

5

1

2

2

2

129

Household and Institutional Furniture and Kitchen Cabinet
Manufacturing

53

22

4

2

0

18

—

4

1

—

1

1

105

Alumina and Aluminum Production and Processing

28

6

35

1

23

4

2

1

0

2

0

0

103

183
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

End Use Not Reported(GWh)

Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

Other Process Use(GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)

Process Cooling And
Refrigeration (GWh)

Process Heating(GWh)

Facility HVAC (GWh)

Machine Drive (GWh)

Subsector
Seafood Product Preparation and Packaging

41

8

4

25

0

7

2

2

3

2

1

0

97

Rubber Product Manufacturing

48

9

14

7

—

9

—

2

0

—

0

—

91

Sugar and Confectionery Product Manufacturing

37

7

4

23

0

6

2

2

3

2

1

0

86

Machine Shops; Turned Product; and Screw, Nut, and Bolt
Manufacturing

37

14

13

3

3

8

3

3

—

—

1

0

85

Engine, Turbine, and Power Transmission Equipment
Manufacturing

35

17

8

3

1

11

2

3

1

—

1

—

81

Electric Lighting Equipment Manufacturing

23

13

19

5

6

8

3

2

0

—

1

—

81

Audio and Video Equipment Manufacturing

18

19

9

9

2

7

6

4

1

2

0

2

78

Spring and Wire Product Manufacturing

32

12

11

2

3

7

3

3

—

—

1

0

73

Lime and Gypsum Product Manufacturing

32

4

15

2

1

3

1

1

1

—

0

0

60

Veneer, Plywood, and Engineered Wood Product
Manufacturing

38

4

3

0

0

3

1

1

1

1

0

0

51

Cutlery and Handtool Manufacturing

18

6

6

1

2

4

2

1

—

—

0

0

41

Grain and Oilseed Milling

16

3

2

10

0

3

1

1

1

1

0

0

38

Ventilation, Heating, Air-Conditioning, and Commercial
Refrigeration Equipment Manufacturing

16

8

4

2

0

5

1

1

0

—

0

—

38

Other Textile Product Mills

19

5

3

2

0

4

0

1

2

—

1

—

37

Motor Vehicle Body and Trailer Manufacturing

14

7

4

2

0

5

2

1

0

1

1

0

37

Animal Food Manufacturing

15

3

2

9

0

3

1

1

1

1

0

0

35

Household Appliance Manufacturing

10

6

8

2

3

3

1

1

0

—

0

—

34

Clay Product and Refractory Manufacturing

18

2

8

1

1

2

1

1

0

—

0

0

34

Manufacturing and Reproducing Magnetic and Optical
Media

6

6

3

3

1

2

2

1

0

1

0

0

26

Agriculture, Construction, and Mining Machinery
Manufacturing

10

5

2

1

0

3

1

1

0

—

0

—

24

Other Transportation Equipment Manufacturing

9

4

2

1

0

3

1

1

0

0

0

0

23

Motor Vehicle Manufacturing

9

4

2

1

0

3

1

1

0

0

0

0

22

Office Furniture (including Fixtures) Manufacturing

8

3

1

0

0

3

—

1

0

—

0

0

16

Cut and Sew Apparel Manufacturing

5

3

1

0

—

2

—

0

0

—

0

—

12

184
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

End Use Not Reported(GWh)

Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

Other Process Use(GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)

Process Cooling And
Refrigeration (GWh)

Process Heating(GWh)

Facility HVAC (GWh)

Machine Drive (GWh)

Subsector
Sawmills and Wood Preservation

8

1

1

0

0

1

0

0

0

0

0

0

11

Other Leather and Allied Product Manufacturing

7

1

1

0

0

1

0

0

0

—

0

—

10

Tobacco Manufacturing

4

1

0

2

0

1

0

0

0

1

0

0

10

Boiler, Tank, and Shipping Container Manufacturing

4

1

1

0

0

1

0

0

—

—

0

0

9

Other Furniture Related Product Manufacturing

3

1

0

0

0

1

—

0

0

—

0

0

7

Footwear Manufacturing

4

1

0

0

0

1

0

0

0

—

0

—

6

Railroad Rolling Stock Manufacturing

2

1

1

0

0

1

0

0

0

0

0

0

5

Textile Furnishings Mills

1

0

0

0

0

0

0

0

0

—

0

—

2

Hardware Manufacturing

1

0

0

0

0

0

0

0

—

—

0

0

1

Leather and Hide Tanning and Finishing
Total

1

0

0

0

0

0

0

0

0

—

0

—

1

15,143

3,214

3,020

2,280

2,168

2,022

758

641

528

329

144

110

30,356185
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-20. Industrial electricity use diurnal patterns by season, modeled for the New Englandcensus division 2012

186
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-22. Distributed Generation Model, Annual Summary the New England Census Division in2012 by Sector and DG Type
Sector/DG Type

Industry

Commercial

7,412

2,581

20

10,013Distributed PV (GWh)

110

109

66

284

Thermal DG (GWh)

18

9

—

28

Total (GWh)

7,540

2,699

85

10,325

CHP (%)

98.3

95.6

23.1

97.0

Distributed PV (%)

1.5

4.0

76.9

2.8

Thermal DG (%)

0.2

0.4

—

0.3

Total (%)

73.0

26.1

0.8

100.0

CHP (GWh)

Residential

Total

Figure G-21. Distributed Generation Model Diurnal Generation Patterns, New Englandcensus division 2012187
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

G.4 Mid Atlantic
Table G-23. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, Mid Atlantic Census Division
ComponentType

Component
Name

Top-down

Hourly load

395.0

DerivedT&D losses

17.3

Top-down

Annual energy

dsgridResidential

Commercial

Industrial

Transport

Total

132.2

161.5

69.5

3.9

367.2

Distributed generation

0.7

9.6

33.7

–

43.9

dsgrid-core

Gap models

30.6

57.0

14.1

3.3

105.0

dsgrid-core

Detailed sector models

110.2

145.2

89.7

–

345.0

DerivedTotal site energy

132.9

171.1

103.2

3.9

411.1

DerivedAnnual sector residuals

-7.9

-31.1

-0.5

0.6

-38.9

DerivedHourly residuals-28.4

Figure G-22. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the Mid Atlantic census division in 2012188
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-23. dsgrid hourly residuals shown in context for the Mid Atlantic census division.

Figure G-24. Historical and dsgrid load duration curves for the Mid Atlantic census division in2012
The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of thedetailed sector and gap models.

189
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-24. Residential Subsectors, Summary of Electricity by End Use for the Mid Atlantic
Census Division in 2012
Interior Interior Space Space
Water Exterior
Heat
Equipment Lights Cooling Heating Fans Systems Lights Pumps Rejection Total(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
Subsector
SingleFamilyDetached

44,354 15,035 14,443 10,818 8,646

7,692

3,095

586

— 104,668

SingleFamilyAttached

6,916

1,514

2,042

899 1,024

1,082

247

64

— 13,788

Apartment
in Building
2 to 4 Units

4,415

1,797

1,542

1,120 1,829

—

2,161

159

54 13,076

MidriseApartment
Building

1,775

785

688

435

791

—

935

80

25

5,514

Mobile
Home

1,866

409

551

242

276

292

67

17

—

3,719

Total

59,325 19,540 19,266 13,515 12,566

9,066

6,505

906

79 140,766

Table G-25. Residential Electricity Proportions by Subsector and End Use for the Mid Atlantic
Census Division in 2012

Subsector

Interior Interior Space Space
Water Exterior
Heat
Equipment Lights Cooling Heating Fans Systems Lights Pumps Rejection Total
(%)
(%)
(%)
(%)
(%)a
(%)
(%)
(%)
(%)
(%)

Single Family
Detached

42.4

14.4

13.8

10.3

8.3

7.3

3.0

0.6

—

74.4

Single Family
Attached

50.2

11.0

14.8

6.5

7.4

7.8

1.8

0.5

—

9.8

Apartment in
Building 2 to
4 Units

33.8

13.7

11.8

8.6 14.0

—

16.5

1.2

0.4

9.3

MidriseApartment
Building

32.2

14.2

12.5

7.9 14.3

—

17.0

1.4

0.5

3.9

Mobile Home

50.2

11.0

14.8

6.5

7.4

7.8

1.8

0.5

—

2.6

Total

42.1

13.9

13.7

9.6

8.9

6.4

4.6

0.6

0.1 100.0

190This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-25. Residential electricity use diurnal patterns by season, modeled for the Mid Atlantic
census division 2012

191
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-26. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the Mid Atlantic Census Division in 2012

Subsector

Interior
Exterior Space Space
Heat
Interior
Lights Equipment Fans Lights Cooling Heating Pumps Rejection(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
(GWh)

Total
(GWh)

Strip Mall

33,372

4,833

7,535

6,797

2,980

1,995

25

10

57,547

Large Office

9,146

10,950

6,715

1,859

5,378

187

819

267

35,320Standalone
Retail Store

7,303

2,311

2,240

1,102

906

1,012

5

2

14,882Medium Office

2,735

3,117

2,044

974

1,288

159

109

34

10,458Small Office

1,918

2,621

1,237

750

702

258

8

5

7,500

Warehouse

1,954

785

758

957

103

927

6

2

5,491

Full Service
Restaurant

1,208

2,720

669

338

371

174

6

2

5,489

Large Hotel

780

1,237

567

196

469

62

36

13

3,360

Hospital

494

686

315

39

246

8

45

17

1,850

Primary School

531

427

236

70

137

72

6

3

1,482

Outpatient
Treatment
Facility

393

592

173

141

101

—

19

8

1,426

Small Hotel

87

133

22

60

12

12

1

0

326

Quick Service
Restaurant

7

39

12

4

6

0

0

0

69

30,452 22,523

13,287

12,699

4,865

1,085

Total

59,927362 145,200

192
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-27. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the Mid Atlantic Census Division in 2012

Subsector

Interior
Exterior Space Space
Heat
Interior
Lights Equipment Fans Lights Cooling Heating Pumps Rejection(%)(%) (%)
(%)
(%)
(%)
(%)
(%)

Total
(%)

Strip Mall

58.0

8.4

13.1

11.8

5.2

3.5

0.0

0.0

39.6

Large Office

25.9

31.0

19.0

5.3

15.2

0.5

2.3

0.8

24.3

Standalone
Retail Store

49.1

15.5

15.1

7.4

6.1

6.8

0.0

0.0

10.2

Medium Office

26.2

29.8

19.5

9.3

12.3

1.5

1.0

0.3

7.2

Small Office

25.6

35.0

16.5

10.0

9.4

3.4

0.1

0.1

5.2

Warehouse

35.6

14.3

13.8

17.4

1.9

16.9

0.1

0.0

3.8

Full Service
Restaurant

22.0

49.6

12.2

6.2

6.8

3.2

0.1

0.0

3.8

Large Hotel

23.2

36.8

16.9

5.8

14.0

1.8

1.1

0.4

2.3

Hospital

26.7

37.1

17.0

2.1

13.3

0.4

2.4

0.9

1.3

Primary School

35.9

28.8

15.9

4.7

9.2

4.9

0.4

0.2

1.0

Outpatient
Treatment
Facility

27.5

41.5

12.1

9.9

7.1

—

1.3

0.5

1.0

Small Hotel

26.5

40.8

6.7

18.4

3.6

3.5

0.4

0.1

0.2

Quick Service
Restaurant

9.7

57.5

18.1

5.7

8.3

0.5

0.0

0.1

0.0

Total

41.3

21.0

15.5

9.2

8.7

3.4

0.7

0.2

100.0

193
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-26. Commercial electricity use diurnal patterns by season, modeled for the Mid Atlantic
census division 2012

194
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Facility Lighting(GWh)

Other Process Use(GWh)Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use
(GWh)

502

2,342

876

1,328

564

212

293

166

189

35

35

14,322

Basic Chemical Manufacturing

5,090

328

1,511

567

857

366

137

189

107

122

23

22

9,320

Iron and Steel Mills and Ferroalloy Manufacturing

2,341

3,006

2,000

544

129

382

135

41

104

171

14

24

8,893

Pulp, Paper, and Paperboard Mills

3,538

184

38

205

78

180

158

216

50

23

9

—

4,680

Plastics Product Manufacturing

2,025

606

—

382

303

357

—

19

86

—

16

—

3,794

Converted Paper Product Manufacturing

2,785

145

30

163

62

142

125

171

40

19

8

—

3,690

Other Chemical Product and Preparation
Manufacturing

1,769

114

528

198

299

127

48

66

37

43

8

8

3,245

Soap, Cleaning Compound, and Toilet Preparation
Manufacturing

1,749

113

535

199

303

128

48

67

38

43

8

8

3,241

Petroleum and Coal Products Manufacturing

2,075

87

37

97

108

65

18

19

25

29

0

3

2,563

Resin, Synthetic Rubber, and Artificial Synthetic
Fibers and Filaments Manufacturing

1,369

88

407

153

231

98

37

51

29

33

6

6

2,507

Other Nonmetallic Mineral Product Manufacturing

940

446

31

114

68

87

38

16

27

—

7

3

1,775

Printing and Related Support Activities

917

71

19

288

127

144

24

14

48

—

19

—

1,671

Pesticide, Fertilizer, and Other Agricultural
Chemical Manufacturing

842

55

258

96

147

62

23

32

18

21

4

4

1,562

Semiconductor and Other Electronic Component
Manufacturing

336

175

33

352

168

134

107

13

71

32

4

28

1,452

Glass and Glass Product Manufacturing

691

327

23

83

49

63

27

11

19

—

5

2

1,301

Paint, Coating, and Adhesive Manufacturing

695

45

212

79

120

51

19

27

15

17

3

3

1,287

195
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)

Process Cooling And
Refrigeration (GWh)7,779

Subsector

Facility HVAC (GWh)Electro Chemical
Processes (GWh)

Pharmaceutical and Medicine Manufacturing

Machine Drive (GWh)

Process Heating(GWh)

Table G-28. Industrial Manufacturing Subsectors, Summary of Model Results for the Mid Atlantic Census Division in 2012

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use
(GWh)

277

76

18

54

19

6

14

24

2

3

1,266

Other Wood Product Manufacturing

799

68

3

81

8

64

12

19

17

12

3

4

1,089

Dairy Product Manufacturing

461

45

2

88

274

78

19

32

21

23

10

4

1,058

Navigational, Measuring, Electromedical, andControl Instruments Manufacturing

213

111

21

228

109

87

70

8

46

21

2

18

934

Beverage Manufacturing

337

40

2

110

184

87

23

15

31

72

19

4

925

Other Miscellaneous Manufacturing

302

107

6

222

66

128

17

15

45

—

5

2

916

Other Electrical Equipment and Component
Manufacturing

260

213

72

153

59

91

34

4

21

—

6

—

913

Architectural and Structural Metals Manufacturing

374

128

34

134

28

84

31

—

29

—

6

2

851

Foundries

220

281

185

51

12

36

13

4

10

16

1

2

830

Bakeries and Tortilla Manufacturing

345

34

2

67

208

59

14

24

16

18

7

3

797

Other Fabricated Metal Product Manufacturing

339

116

30

120

25

76

28

—

26

—

5

2

767

Cement and Concrete Product Manufacturing

395

188

13

48

29

37

16

7

11

—

3

1

749

Fruit and Vegetable Preserving and Specialty
Food Manufacturing

317

31

2

62

192

54

13

23

15

16

7

3

734

Animal Slaughtering and Processing

297

29

2

58

180

51

12

21

14

15

6

3

689

Other Food Manufacturing

244

24

1

47

146

41

10

17

11

12

5

2

561

Motor Vehicle Parts Manufacturing

208

59

3

101

27

68

27

4

21

8

8

6

542

Other General Purpose Machinery Manufacturing

208

48

5

102

20

70

12

5

18

—

5

—

493

Medical Equipment and Supplies Manufacturing

156

55

3

114

33

66

9

7

23

—

3

1

470

196
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)433

Facility HVAC (GWh)

Process Heating
(GWh)

340

Machine Drive (GWh)

Nonferrous Metal (except Aluminum) Production
and Processing

Subsector

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use
(GWh)

101

28

7

20

7

2

5

9

1

1

461

Fabric Mills

257

37

0

65

16

32

8

9

7

—

4

—

433

Forging and Stamping

185

64

17

66

14

42

15

—

14

—

3

1

421

Coating, Engraving, Heat Treating, and Allied
Activities

184

63

17

67

14

42

16

—

14

—

3

1

420

Communications Equipment Manufacturing

93

49

9

100

48

38

31

4

20

9

1

8

409

Aerospace Product and Parts Manufacturing

150

43

3

73

20

49

19

3

15

6

5

5

390

Clay Product and Refractory Manufacturing

204

97

7

25

15

19

8

3

6

—

1

1

388

Electrical Equipment Manufacturing

107

89

31

65

25

38

14

2

9

—

3

—

383

Fiber, Yarn, and Thread Mills

225

32

0

56

14

27

7

8

6

—

3

—

378

Industrial Machinery Manufacturing

159

37

4

77

15

52

9

4

14

—

4

—

373

Alumina and Aluminum Production andProcessing

97

124

80

22

5

15

5

2

4

7

1

1

364

Sugar and Confectionery Product Manufacturing

150

15

1

29

92

26

6

11

7

8

3

1

349

Commercial and Service Industry Machinery
Manufacturing

144

33

3

69

14

47

8

3

12

—

3

—

338

Metalworking Machinery Manufacturing

140

32

3

68

14

47

8

3

12

—

3

—

332

Engine, Turbine, and Power Transmission
Equipment Manufacturing

135

31

3

65

13

45

7

3

12

—

3

—

318

Railroad Rolling Stock Manufacturing

106

30

2

52

14

35

14

2

11

4

4

3

278

Animal Food Manufacturing

119

12

1

23

73

20

5

9

6

6

3

1

277

Textile and Fabric Finishing and Fabric Coating
Mills

162

23

0

41

10

20

5

6

4

—

2

—

273

197
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)157

Facility HVAC (GWh)

Process Heating
(GWh)

124

Machine Drive (GWh)Steel Product Manufacturing from Purchased
Steel

Subsector

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use
(GWh)

—

27

21

25

—

1

6

—

1

—

266

Veneer, Plywood, and Engineered Wood Product
Manufacturing

172

15

1

17

2

14

2

4

4

2

1

1

233

Household and Institutional Furniture and Kitchen
Cabinet Manufacturing

105

8

1

43

4

36

—

3

7

—

2

1

209

Lime and Gypsum Product Manufacturing

97

46

3

12

7

9

4

2

3

—

1

0

184

Motor Vehicle Body and Trailer Manufacturing

67

19

1

33

9

22

9

1

7

3

2

2

175

Other Transportation Equipment Manufacturing

66

19

1

32

9

22

9

1

7

3

2

2

174

Machine Shops; Turned Product; and Screw, Nut,and Bolt Manufacturing

73

25

7

26

6

17

6

—

6

—

1

0

167

Computer and Peripheral Equipment
Manufacturing

36

19

4

40

19

15

12

1

8

4

0

3

163

Grain and Oilseed Milling

70

7

0

14

42

12

3

5

3

4

1

1

162

Agriculture, Construction, and Mining Machinery
Manufacturing

66

15

2

32

6

22

4

1

6

—

2

—

155

Spring and Wire Product Manufacturing

57

20

5

21

4

13

5

—

5

—

1

0

131

Manufacturing and Reproducing Magnetic and
Optical Media

30

15

3

31

15

12

10

1

6

3

0

2

129

Electric Lighting Equipment Manufacturing

34

28

9

20

8

12

4

1

3

—

1

—

120

Audio and Video Equipment Manufacturing

25

13

2

26

12

10

8

1

5

2

0

2

108

Cut and Sew Apparel Manufacturing

36

5

—

26

2

15

—

2

3

—

1

—

90

Ship and Boat Building

34

10

1

17

5

11

5

1

4

1

1

1

90

198
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)42

Facility HVAC (GWh)

Process Heating
(GWh)

142

Machine Drive (GWh)

Rubber Product Manufacturing

Subsector

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use
(GWh)

1

17

3

12

2

1

3

—

1

—

84

Boiler, Tank, and Shipping Container
Manufacturing

34

12

3

12

2

7

3

—

3

—

1

0

76

Other Textile Product Mills

38

5

0

9

5

8

0

4

2

—

2

—

73

Motor Vehicle Manufacturing

27

8

0

14

4

9

4

1

3

1

1

1

72

Office Furniture (including Fixtures) Manufacturing

35

3

0

15

1

12

—

1

2

—

1

0

71

Tobacco Manufacturing

23

3

0

7

12

6

2

1

2

5

1

0

61

Cutlery and Handtool Manufacturing

22

8

2

8

2

5

2

—

2

—

0

0

50

Seafood Product Preparation and Packaging

16

2

0

3

10

3

1

1

1

1

0

0

36

Household Appliance Manufacturing

10

8

3

6

2

3

1

0

1

—

0

—

35

Sawmills and Wood Preservation

19

2

0

2

0

2

0

0

0

0

0

0

26

Other Leather and Allied Product Manufacturing

12

2

0

2

1

2

0

0

0

—

0

—

19

Other Furniture Related Product Manufacturing

8

1

0

3

0

3

—

0

1

—

0

0

16

Footwear Manufacturing

4

1

0

1

0

1

0

0

0

—

0

—

7

Textile Furnishings Mills

4

0

0

1

0

1

0

0

0

—

0

—

7

Leather and Hide Tanning and Finishing

2

0

0

0

0

0

0

0

0

—

0

—

4

Hardware Manufacturing

1

0

0

0

0

0

0

—

0

—

0

0

3

Apparel Accessories and Other Apparel
Manufacturing

0

0

—

0

0

0

—

0

0

—

0

—

1

44,898

9,637

9,001

7,770

6,633

5,134

1,823

1,565

1,553

1,055

349

249

89,667

Total

199
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)8Facility HVAC (GWh)

Process Heating
(GWh)
36

Machine Drive (GWh)

Ventilation, Heating, Air-Conditioning, andCommercial Refrigeration Equipment
Manufacturing

Subsector

Figure G-27. Industrial electricity use diurnal patterns by season, modeled for the Mid Atlantic
census division 2012

200
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-29. Distributed Generation Model, Annual Summary the Mid Atlantic Census Division in2012 by Sector and DG Type
Sector/DG TypeCHP (GWh)

Industry

Commercial

Residential

Total

33,0969,015

349

42,460Distributed PV (GWh)

542

544

318

1,403

Thermal DG (GWh)

36

40

—

76

33,674

9,599

667

43,940

CHP (%)

98.3

93.9

52.3

96.6

Distributed PV (%)

1.6

5.7

47.7

3.2

Thermal DG (%)

0.1

0.4

—

0.2

Total (%)

76.6

21.8

1.5

100.0

Total (GWh)

Figure G-28. Distributed Generation Model Diurnal Generation Patterns, Mid Atlantic
census division 2012

201
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

G.5 South Atlantic
Table G-30. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, South Atlantic Census Division
ComponentType

Component
Name

Top-down

Hourly load

810.7

DerivedT&D losses

41.8

Top-down

Annual energy

dsgridDistributed generation

dsgrid-core

Residential

Commercial

Industrial

Transport

Total

336.8

305.6

139.4

1.3

783.0

–

3.0

30.7

–

33.6

Gap models

63.8

91.8

27.3

0.8

183.7

dsgrid-core

Detailed sector models

271.1

251.1

174.0

–

696.2

DerivedTotal site energy

336.8

308.6

170.0

1.3

816.7

DerivedAnnual sector residuals

1.9

-34.4

-31.3

0.5

-63.3

DerivedHourly residuals-79.2

Figure G-29. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the South Atlantic census division in 2012202
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-30. dsgrid hourly residuals shown in context for the South Atlantic census division.

Figure G-31. Historical and dsgrid load duration curves for the South Atlantic census division in2012
The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of thedetailed sector and gap models.

203
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-31. Residential Subsectors, Summary of Electricity by End Use for the South Atlantic
Census Division in 2012
Interior
Space Space Interior Water
Exterior
Heat
Equipment Cooling Heating Lights Systems Fans Lights Pumps Rejection Total(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
Subsector
SingleFamilyDetached

89,096 61,900 32,942 28,115

27,691

MobileHome

11,787

6,684

2,873

2,423

SingleFamilyAttached

8,164

4,630

1,990

Apartmentin
Building 2
to 4 Units

4,030

2,083

MidriseApartment
Building

1,081

613

Total

5,716

39

—

3,965 2,322

394

7— 30,454

1,678

2,747 1,608

273

5— 21,094

466

1,735

— 1,640

2,083

164

74 12,274

131

485

461

578

52

24

34,403 28,173

9,043

266

98 334,886

22,142—

114,157 75,909 38,402 34,435

267,640

3,424

Table G-32. Residential Electricity Proportions by Subsector and End Use for the South Atlantic
Census Division in 2012

Subsector

Interior
Space Space Interior Water
Exterior
Heat
Equipment Cooling Heating Lights Systems Fans Lights Pumps Rejection Total
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)

Single Family
Detached

33.3

23.1

12.3

10.5

10.3

8.3

2.1

0.0

—

79.9

Mobile Home

38.7

21.9

9.4

8.0

13.0

7.6

1.3

0.0

—

9.1

Single Family
Attached

38.7

21.9

9.4

8.0

13.0

7.6

1.3

0.0

—

6.3

Apartment in
Building 2 to
4 Units

32.8

17.0

3.8

14.1

—

13.4

17.0

1.3

0.6

3.7

MidriseApartment
Building

31.6

17.9

3.8

14.2

—

13.5

16.9

1.5

0.7

1.0

Total

34.1

22.7

11.5

10.3

10.3

8.4

2.7

0.1

0.0 100.0

204This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-32. Residential electricity use diurnal patterns by season, modeled for the South Atlantic
census division 2012

205
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-33. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the South Atlantic Census Division in 2012

Subsector

Space
Exterior Space
Heat
Interior InteriorLights Equipment Cooling Fans Lights Heating Pumps Rejection Total(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
(GWh)
(GWh)

Large Office

18,354

21,049 16,108 14,570

3,736

255

2,149

915

77,135

Strip Mall

30,416

4,409

5,428

5,947

6,198

1,019

26

13

53,456

Standalone
Retail Store

17,273

5,332

4,287

4,651

2,607

915

13

6

35,085

Medium Office

4,289

4,902

3,473

3,017

1,528

85

208

89

17,591

Small Office

4,150

5,696

2,808

2,813

1,622

196

25

18

17,328Large Hotel

2,961

4,783

3,259

2,694

761

76

168

74

14,774

Warehouse

4,398

1,746

513

1,140

2,099

631

10

8

10,544

Full Service
Restaurant

2,122

4,733

1,377

1,193

592

105

14

6

10,143

Hospital

1,678

2,131

1,715

1,153

126

—

258

146

7,206

Primary
School

1,160

925

625

558

149

46

21

13

3,496

Outpatient
Treatment
Facility

912

1,340

311

338

328

—

48

26

3,303

Small Hotel

248

343

45

39

163

11

5

3

857

Quick Service
Restaurant

19

115

33

37

11

1

0

0

217

57,504 39,982 38,150

19,920

3,340

2,943

Total

87,9791,318 251,137

206
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Table G-34. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the South Atlantic Census Division in 2012

Subsector

Space
Exterior Space
Heat
Interior InteriorLights Equipment Cooling Fans Lights Heating Pumps Rejection
(%)
(%)
(%)
(%)
(%)
(%) (%)
(%)

Total
(%)

Large Office

23.8

27.3

20.9

18.9

4.8

0.3

2.8

1.2

30.7

Strip Mall

56.9

8.2

10.2

11.1

11.6

1.9

0.0

0.0

21.3

Standalone
Retail Store

49.2

15.2

12.2

13.3

7.4

2.6

0.0

0.0

14.0

Medium Office

24.4

27.9

19.7

17.2

8.7

0.5

1.2

0.5

7.0

Small Office

24.0

32.9

16.2

16.2

9.4

1.1

0.1

0.1

6.9

Large Hotel

20.0

32.4

22.1

18.2

5.1

0.5

1.1

0.5

5.9

Warehouse

41.7

16.6

4.9

10.8

19.9

6.0

0.1

0.1

4.2

Full Service
Restaurant

20.9

46.7

13.6

11.8

5.8

1.0

0.1

0.1

4.0

Hospital

23.3

29.6

23.8

16.0

1.7

—

3.6

2.0

2.9

Primary
School

33.2

26.5

17.9

16.0

4.3

1.3

0.6

0.4

1.4

Outpatient
Treatment
Facility

27.6

40.6

9.4

10.2

9.9

—

1.4

0.8

1.3

Small Hotel

28.9

40.0

5.3

4.5

19.0

1.3

0.6

0.3

0.3

Quick Service
Restaurant

8.9

53.0

15.2

17.0

5.2

0.4

0.1

0.1

0.1

Total

35.0

22.9

15.9

15.2

7.9

1.3

1.2

0.5

100.0

207
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-33. Commercial electricity use diurnal patterns by season, modeled for the South
Atlantic census division 2012

208
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Other Process Use(GWh)Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

End Use Not Reported(GWh)

Onsite Transportation
(GWh)

172

351

805

708

967

225

105

43

— 20,988

Iron and Steel Mills and Ferroalloy
Manufacturing

3,372

4,329

784

2,879

186

550

195

59

150

247

21

35 12,806

Petroleum and Coal Products Manufacturing

9,798

410

460

176

512

306

86

90

119

136

1

12 12,106

Basic Chemical Manufacturing

6,211

400

692

1,844

1,046

446

167

231

131

149

28

27 11,372

Plastics Product Manufacturing

5,056

1,513

955

—

756

893

—

47

215

—

40

—

9,475

Converted Paper Product Manufacturing

6,491

338

380

71

145

331

293

400

93

43

18

—

8,603

Pharmaceutical and Medicine Manufacturing

4,378

283

493

1,318

748

317

119

165

94

106

20

20

8,060

Resin, Synthetic Rubber, and Artificial Synthetic
Fibers and Filaments Manufacturing

2,691

173

300

800

454

194

72

100

57

65

12

12

4,930

Fiber, Yarn, and Thread Mills

2,849

408

709

0

176

348

86

98

71

—

41

—

4,788

Pesticide, Fertilizer, and Other Agricultural
Chemical Manufacturing

2,470

160

282

759

430

181

69

95

54

61

11

11

4,584

Animal Slaughtering and Processing

1,919

190

373

10

1,165

328

80

137

89

98

41

19

4,450

Other Wood Product Manufacturing

3,222

274

325

13

31

260

47

77

69

47

11

15

4,389

Other Chemical Product and Preparation
Manufacturing

1,938

125

217

578

328

140

52

72

41

47

9

9

3,556

Printing and Related Support Activities

1,936

149

609

41

267

304

51

30

101

—

39

—

3,527

Other Nonmetallic Mineral Product
Manufacturing

1,728

820

209

57

126

160

70

29

49

—

12

5

3,266

209
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)

Facility Lighting
(GWh)

921

Other Nonprocess
Use (GWh)

Process Cooling And
Refrigeration (GWh)824

Facility HVAC (GWh)

15,867

Subsector

Process Heating(GWh)

Pulp, Paper, and Paperboard Mills

Machine Drive (GWh)Electro Chemical
Processes (GWh)

Table G-35. Industrial Manufacturing Subsectors, Summary of Model Results for the South Atlantic Census Division in 2012

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

427

242

102

39

54

30

35

6

6

2,588

Fabric Mills

1,526

219

385

0

96

189

47

53

39

—

23

—

2,576

Nonferrous Metal (except Aluminum)
Production and Processing

648

824

145

527

34

102

36

11

28

45

4

6

2,410

Motor Vehicle Parts Manufacturing

851

242

413

14

111

277

108

18

87

35

31

26

2,214

1,113

333

211

—

167

197

—

10

48

—

9

—

2,088

869

298

312

78

65

196

72

—

67

—

14

4

1,975

Veneer, Plywood, and Engineered Wood
Product Manufacturing

1,408

119

140

5

13

112

20

33

29

20

5

6

1,910

Paint, Coating, and Adhesive Manufacturing

1,011

65

115

308

175

74

28

39

22

25

5

5

1,871

Aerospace Product and Parts Manufacturing

702

199

340

12

91

228

89

15

72

28

26

22

1,824

Other Fabricated Metal Product Manufacturing

735

252

261

65

54

165

61

—

56

—

11

3

1,664

Other Electrical Equipment and Component
Manufacturing

470

385

278

130

107

164

61

8

38

—

12

—

1,653

Cement and Concrete Product Manufacturing

847

403

103

28

62

79

34

14

24

—

6

3

1,605

Beverage Manufacturing

580

69

190

3

317

150

40

26

53

123

33

7

1,591

Alumina and Aluminum Production andProcessing

400

510

90

328

21

64

22

7

17

28

2

4

1,494

Glass and Glass Product Manufacturing

774

366

93

25

55

71

31

13

22

—

5

2

1,458

Rubber Product Manufacturing
Architectural and Structural Metals
Manufacturing

210
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)159

Facility HVAC (GWh)90

Process Heating
(GWh)

1,397

Machine Drive (GWh)

Soap, Cleaning Compound, and Toilet
Preparation Manufacturing

Subsector

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

3

352

100

24

41

27

30

12

6

1,353

Textile and Fabric Finishing and Fabric Coating
Mills

746

107

189

0

47

93

23

26

19

—

11

—

1,262

Bakeries and Tortilla Manufacturing

541

54

105

3

326

92

23

38

25

28

12

5

1,250

Semiconductor and Other Electronic
Component Manufacturing

287

149

301

28

143

115

91

11

61

27

3

24

1,241

Foundries

322

412

74

271

18

52

18

6

14

23

2

3

1,216

Fruit and Vegetable Preserving and Specialty
Food Manufacturing

512

51

100

3

311

88

21

36

24

26

11

5

1,187

Motor Vehicle Manufacturing

441

126

218

8

59

145

57

10

46

18

16

14

1,158

Household and Institutional Furniture andKitchen Cabinet Manufacturing

539

40

221

4

18

183

—

13

37

—

12

5

1,072

Dairy Product Manufacturing

458

45

88

2

272

77

19

32

21

23

10

4

1,052

Navigational, Measuring, Electromedical, andControl Instruments Manufacturing

236

123

252

24

121

96

77

9

51

23

3

20

1,034

Other Miscellaneous Manufacturing

322

114

237

7

70

137

19

16

48

—

6

2

976

Other Textile Product Mills

490

65

121

3

61

98

4

46

23

—

23

—

933

Ship and Boat Building

307

88

155

5

42

103

41

7

33

13

12

10

815

Other General Purpose Machinery
Manufacturing

309

72

151

8

30

103

17

7

27

—

7

—

732

Coating, Engraving, Heat Treating, and Allied
Activities

320

110

116

29

24

73

27

—

25

—

5

2

731

211
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)113

Facility HVAC (GWh)58

Process Heating
(GWh)

588

Machine Drive (GWh)Other Food Manufacturing

Subsector

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

57

47

71

27

3

17

—

5

—

715

Communications Equipment Manufacturing

160

84

172

16

83

65

53

6

35

16

2

14

707

Animal Food Manufacturing

292

29

57

2

179

50

12

21

14

15

6

3

681

Steel Product Manufacturing from Purchased
Steel

179

227

40

146

9

28

10

3

8

13

1

2

666

Engine, Turbine, and Power Transmission
Equipment Manufacturing

274

63

132

7

26

90

15

6

24

—

6

—

644

Industrial Machinery Manufacturing

262

60

126

6

25

86

14

6

23

—

6

—

615

Tobacco Manufacturing

211

25

68

1

112

54

14

9

19

44

12

2

571

Commercial and Service Industry Machinery
Manufacturing

219

51

106

5

21

72

12

5

19

—

5

—

515

Medical Equipment and Supplies Manufacturing

167

59

121

4

36

70

9

8

24

—

3

1

502

Lime and Gypsum Product Manufacturing

265

126

32

9

19

25

11

4

8

—

2

1

501

Forging and Stamping

198

68

71

18

15

44

16

—

15

—

3

1

449

Sugar and Confectionery Product
Manufacturing

183

18

36

1

111

31

8

13

9

9

4

2

424

Clay Product and Refractory Manufacturing

222

106

27

8

16

21

9

4

6

—

2

1

422

Motor Vehicle Body and Trailer Manufacturing

141

40

69

2

19

46

18

3

15

6

5

4

368

Grain and Oilseed Milling

158

16

30

1

95

27

7

11

7

8

3

2

364

Agriculture, Construction, and Mining Machinery
Manufacturing

134

31

65

3

13

44

7

3

12

—

3

—

315

212
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)121

Facility HVAC (GWh)

166

Process Heating
(GWh)

201

Machine Drive (GWh)Electrical Equipment Manufacturing

Subsector

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

1

21

33

1

15

8

—

8

—

313

Ventilation, Heating, Air-Conditioning, andCommercial Refrigeration Equipment
Manufacturing

128

30

63

3

12

43

7

3

11

—

3

—

303

Seafood Product Preparation and Packaging

126

13

25

1

78

22

5

9

6

7

3

1

294

Computer and Peripheral Equipment
Manufacturing

66

35

72

7

35

27

22

3

15

7

1

6

294

Cut and Sew Apparel Manufacturing

111

14

80

—

5

47

—

6

9

—

2

—

274

Metalworking Machinery Manufacturing

106

25

52

3

10

35

6

2

9

—

3

—

251

Spring and Wire Product Manufacturing

106

36

38

10

8

24

9

—

8

—

2

1

242

Other Transportation Equipment Manufacturing

85

24

42

1

11

28

11

2

9

3

3

3

223

Household Appliance Manufacturing

62

51

36

17

14

22

8

1

5

—

2

—

217

Electric Lighting Equipment Manufacturing

57

46

33

15

13

20

7

1

5

—

1

—

198

Boiler, Tank, and Shipping Container
Manufacturing

80

27

28

7

6

18

6

—

6

—

1

0

180

Sawmills and Wood Preservation

126

11

13

0

1

10

2

3

3

2

0

1

171

Railroad Rolling Stock Manufacturing

65

18

32

1

9

21

8

1

7

3

2

2

170

Office Furniture (including Fixtures)
Manufacturing

84

6

35

1

3

29

—

2

6

—

2

1

168

Machine Shops; Turned Product; and Screw,
Nut, and Bolt Manufacturing

47

16

17

4

4

11

4

—

4

—

1

0

107

213
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)41

Facility HVAC (GWh)22

Process Heating
(GWh)

164

Machine Drive (GWh)

Textile Furnishings Mills

Subsector

Process Cooling And
Refrigeration (GWh)

Facility Lighting(GWh)

Other Process Use(GWh)Conventional Boiler
Use (GWh)

Other Facility Support(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

19

0

2

16

—

1

3

—

1

0

93

Audio and Video Equipment Manufacturing

16

8

17

2

8

6

5

1

3

2

0

1

70

Manufacturing and Reproducing Magnetic and
Optical Media

15

8

16

1

7

6

5

1

3

1

0

1

64

Cutlery and Handtool Manufacturing

23

8

8

2

2

5

2

—

2

—

0

0

52

Other Leather and Allied Product Manufacturing

16

2

3

0

1

3

0

0

1

—

0

—

26

Footwear Manufacturing

5

1

1

0

0

1

0

0

0

—

0

—

9

Hardware Manufacturing

4

1

1

0

0

1

0

—

0

—

0

0

8

Apparel Accessories and Other Apparel
Manufacturing

2

0

2

—

0

1

—

0

0

—

0

—

6

Leather and Hide Tanning and Finishing

2

0

0

0

0

0

0

0

0

—

0

—

4

Apparel Knitting Mills

1

0

1

—

0

0

—

0

0

—

0

—

2

94,480 17,959 15,331 11,431 11,302 10,543

3,599

3,354

3,047

1,788

787

Total

Process Heating
(GWh)

214
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)3

Facility HVAC (GWh)47

Machine Drive (GWh)Other Furniture Related Product Manufacturing

Subsector

409 174,029

Figure G-34. Industrial electricity use diurnal patterns by season, modeled for the South Atlantic
census division 2012

215
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-36. Distributed Generation Model, Annual Summary the South Atlantic Census Divisionin 2012 by Sector and DG Type
Sector/DG TypeCHP (GWh)

Industry

Commercial

Residential

Total

30,0002,751

Thermal DG (GWh)

553

111

Distributed PV (GWh)

114

114

86

314

30,6672,976

86

33,730

CHP (%)

97.8

92.4

—

97.1

Thermal DG (%)

1.8

3.7

—

2.0

Distributed PV (%)

0.4

3.8

100.0

0.9

Total (%)

90.9

8.8

0.3

100.0

Total (GWh)—

32,752
664

Figure G-35. Distributed Generation Model Diurnal Generation Patterns, South Atlantic
census division 2012

216
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G.6 East South Central
Table G-37. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, East South Central Census Division
ComponentType

Component
Name

Top-down

Hourly load

332.8

DerivedT&D losses

17.8

Top-down

Annual energy

dsgridDistributed generation

dsgrid-core

Residential

Commercial

Industrial

Transport

Total

114.5

82.8

123.2

0.0

320.5

–

0.5

17.9

–

18.3

Gap models

16.6

25.6

11.3

0.0

53.5

dsgrid-core

Detailed sector models

88.8

54.7

89.4

–

232.9

DerivedTotal site energy

114.5

83.2

141.1

0.0

338.8

DerivedAnnual sector residuals

9.0

3.0

40.4

0.0

52.4

DerivedHourly residuals

33.2

Figure G-36. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the East South Central census division in 2012

217
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-37. dsgrid hourly residuals shown in context for the East South Central census division.

Figure G-38. Historical and dsgrid load duration curves for the East South Central census division in2012
The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of thedetailed sector and gap models.

218
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-38. Residential Subsectors, Summary of Electricity by End Use for the East South Central
Census Division in 2012
Interior
Space Space Interior Water
Exterior
Heat
Equipment Cooling Heating Lights Systems Fans Lights Pumps Rejection Total
Subsector(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh)SingleFamilyDetached

29,574 17,358 14,166

Mobile
Home

4,398

2,137

1,464

906

1,402

Apartment
in Building
2 to 4 Units

1,144

577

120

490

SingleFamilyAttached

716

348

238

MidriseApartment
Building

145

79

15

Total

9,205

8,997 7,185

1,864

10

— 88,359

852

147

2

— 11,307

—

502

592

46

15

3,485

147

228

139

24

0

—

1,841

64

—

71

76

7

2

459

10,628 8,749

2,704

64

17 105,451

35,977 20,498 16,003 10,811

Table G-39. Residential Electricity Proportions by Subsector and End Use for the East South
Central Census Division in 2012

Subsector

Interior
Space Space Interior Water
Exterior
Heat
Equipment Cooling Heating Lights Systems Fans Lights Pumps Rejection Total
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)

Single Family
Detached

33.5

19.6

16.0

10.4

10.2

8.1

2.1

0.0

—

83.8

Mobile Home

38.9

18.9

12.9

8.0

12.4

7.5

1.3

0.0

—

10.7

Apartment in
Building 2 to
4 Units

32.8

16.6

3.4

14.1

—

14.4

17.0

1.3

0.4

3.3

Single Family
Attached

38.9

18.9

12.9

8.0

12.4

7.5

1.3

0.0

—

1.7

MidriseApartment
Building

31.7

17.2

3.2

13.9

—

15.5

16.6

1.5

0.5

0.4

Total

34.1

19.4

15.2

10.3

10.1

8.3

2.6

0.1

0.0 100.0

219This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-39. Residential electricity use diurnal patterns by season, modeled for the East South
Central census division 2012

220
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-40. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the East South Central Census Division in 2012

Subsector

Interior
Space Exterior Space
Heat
Interior
Lights Equipment Fans Cooling Lights Heating Pumps Rejection(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
(GWh)

Total
(GWh)

Strip Mall

8,545

1,240

1,875

1,419

1,750

442

7

3

15,281

Standalone
Retail Store

5,387

1,778

1,613

1,270

819

350

5

2

11,224Large Office

2,289

2,642

1,635

1,758

441

40

239

87

9,131

Small Office

1,060

1,456

744

647

414

72

6

4

4,403

Medium Office

861

985

605

619

305

18

44

16

3,452

Warehouse

1,045

416

330

102

514

267

2

1

2,678

Full Service
Restaurant

517

1,172

303

295

145

45

3

1

2,481

Large Hotel

516

793

442

477

131

26

24

9

2,419

Hospital

372

521

294

338

30

—

59

30

1,645

Primary School

325

256

147

144

41

27

4

2

946

Outpatient
Treatment
Facility

213

312

60

43

76

—

7

3

714

Small Hotel

72

97

4

3

42

6

1

0

225

Quick Service
Restaurant

6

34

13

10

3

0

0

0

66

21,20911,702

8,064

7,124

4,710

1,292

401

160

54,663Total

221
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-41. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the East South Central Census Division in 2012

Subsector

Interior
Space Exterior Space
Heat
Interior
Lights Equipment Fans Cooling Lights Heating Pumps Rejection
(%)
(%)
(%)
(%)
(%)
Total (%)
(%)
(%)
(%)

Strip Mall

55.9

8.1

12.3

9.3

11.5

2.9

0.0

0.0

28.0

Standalone
Retail Store

48.0

15.8

14.4

11.3

7.3

3.1

0.0

0.0

20.5

Large Office

25.1

28.9

17.9

19.3

4.8

0.4

2.6

1.0

16.7

Small Office

24.1

33.1

16.9

14.7

9.4

1.6

0.1

0.1

8.1

Medium Office

24.9

28.5

17.5

17.9

8.8

0.5

1.3

0.5

6.3

Warehouse

39.0

15.5

12.3

3.8

19.2

10.0

0.1

0.0

4.9

Full Service
Restaurant

20.9

47.2

12.2

11.9

5.9

1.8

0.1

0.0

4.5

Large Hotel

21.3

32.8

18.3

19.7

5.4

1.1

1.0

0.4

4.4

Hospital

22.6

31.7

17.9

20.5

1.8

—

3.6

1.8

3.0

Primary School

34.4

27.0

15.5

15.3

4.3

2.8

0.5

0.2

1.7

Outpatient
Treatment
Facility

29.8

43.7

8.3

6.0

10.7

—

1.0

0.5

1.3

Small Hotel

32.1

43.0

1.9

1.5

18.7

2.5

0.2

0.1

0.4

Quick Service
Restaurant

8.7

51.8

19.3

15.0

5.1

0.0

0.1

0.0

0.1

Total

38.8

21.4

14.8

13.0

8.6

2.4

0.7

0.3

100.0

222
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-40. Commercial electricity use diurnal patterns by season, modeled for the East South
Central census division 2012

223
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Facility Lighting(GWh)

Process Cooling And
Refrigeration (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation
(GWh)

4,813

871

3,202

612

207

217

167

66

274

23

38 14,239

Pulp, Paper, and Paperboard Mills

6,809

354

395

74

345

151

304

97

415

45

18

—

9,006

Petroleum and Coal Products Manufacturing

5,907

247

277

106

185

308

52

72

54

82

1

8

7,299

Basic Chemical Manufacturing

3,437

221

383

1,021

247

579

92

72

128

82

15

15

6,294

Converted Paper Product Manufacturing

3,477

181

203

38

178

78

157

50

214

23

9

—

4,608

Plastics Product Manufacturing

2,103

629

397

—

371

314

—

89

20

—

17

—

3,941

Motor Vehicle Parts Manufacturing

1,292

367

628

22

420

169

165

132

28

52

47

40

3,362

Resin, Synthetic Rubber, and Artificial Synthetic
Fibers and Filaments Manufacturing

1,819

117

203

541

131

307

49

38

68

44

8

8

3,333

Animal Slaughtering and Processing

988

98

192

5

169

600

41

46

70

51

21

10

2,291

Other Wood Product Manufacturing

1,666

141

168

7

134

16

24

36

40

24

6

8

2,269

Foundries

489

626

113

412

79

27

28

22

8

35

3

5

1,846

Pesticide, Fertilizer, and Other Agricultural
Chemical Manufacturing

966

63

110

297

71

168

27

21

37

24

4

4

1,792

Motor Vehicle Manufacturing

676

193

334

12

223

90

88

71

15

28

25

21

1,776

Nonferrous Metal (except Aluminum)
Production and Processing

433

551

97

352

68

23

24

18

7

30

3

4

1,611

Alumina and Aluminum Production andProcessing

413

526

93

339

66

22

23

18

7

29

2

4

1,541

224
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)3,749

Other Nonprocess
Use (GWh)

Process Heating(GWh)

Iron and Steel Mills and Ferroalloy
Manufacturing

Subsector

Facility HVAC (GWh)

Machine Drive (GWh)

Table G-42. Industrial Manufacturing Subsectors, Summary of Model Results for the East South Central Census Division in 2012

Facility Lighting(GWh)

Process Cooling And
Refrigeration (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

208

50

118

19

15

26

17

3

3

1,276

Rubber Product Manufacturing

666

199

126

—

118

100

—

28

6

—

5

—

1,250

Printing and Related Support Activities

664

51

209

14

104

92

18

35

10

—

13

—

1,210

Other Nonmetallic Mineral Product
Manufacturing

587

279

71

19

54

43

24

17

10

—

4

2

1,110

Steel Product Manufacturing from Purchased
Steel

232

295

52

189

37

12

13

10

4

16

1

2

864

Pharmaceutical and Medicine Manufacturing

447

29

50

134

32

76

12

10

17

11

2

2

822

Architectural and Structural Metals
Manufacturing

352

121

126

32

79

26

29

27

—

—

6

2

801

Other Fabricated Metal Product Manufacturing

332

114

118

29

74

25

27

25

—

—

5

2

752

Cement and Concrete Product Manufacturing

392

186

48

13

37

29

16

11

7

—

3

1

743

Beverage Manufacturing

251

30

82

1

65

137

17

23

11

53

14

3

688

Veneer, Plywood, and Engineered Wood
Product Manufacturing

505

43

50

2

40

5

7

11

12

7

2

2

685

Soap, Cleaning Compound, and Toilet
Preparation Manufacturing

357

23

41

109

26

62

10

8

14

9

2

2

662

Bakeries and Tortilla Manufacturing

273

27

53

1

46

164

11

13

19

14

6

3

630

Fiber, Yarn, and Thread Mills

362

52

90

0

44

22

11

9

12

—

5

—

609

Household Appliance Manufacturing

163

134

96

45

57

37

21

13

3

—

4

—

571

225
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)78

Facility HVAC (GWh)45

Process Heating
(GWh)

696

Machine Drive (GWh)Other Chemical Product and Preparation
Manufacturing

Subsector

Facility Lighting(GWh)

Process Cooling And
Refrigeration (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

87

21

49

8

6

11

7

1

1

527

Glass and Glass Product Manufacturing

263

124

32

9

24

19

10

7

4

—

2

1

495

Motor Vehicle Body and Trailer Manufacturing

179

51

88

3

59

24

23

19

4

7

7

6

468

Other Electrical Equipment and Component
Manufacturing

128

105

76

36

45

29

17

10

2

—

3

—

452

Household and Institutional Furniture andKitchen Cabinet Manufacturing

223

17

92

2

76

8

—

15

5

—

5

2

444

Other Food Manufacturing

189

19

36

1

32

113

8

9

13

10

4

2

436

Animal Food Manufacturing

183

18

36

1

32

113

8

9

13

10

4

2

427

Fruit and Vegetable Preserving and Specialty
Food Manufacturing

178

18

35

1

30

108

7

8

13

9

4

2

413

Dairy Product Manufacturing

176

17

34

1

30

105

7

8

12

9

4

2

404

Navigational, Measuring, Electromedical, andControl Instruments Manufacturing

91

47

97

9

37

46

30

20

4

9

1

8

398

Other General Purpose Machinery
Manufacturing

165

38

81

4

55

16

9

15

4

—

4

—

391

Textile and Fabric Finishing and Fabric Coating
Mills

224

32

57

0

28

14

7

6

8

—

3

—

380

Aerospace Product and Parts Manufacturing

145

41

70

2

47

19

18

15

3

6

5

4

377

Forging and Stamping

160

55

57

14

36

12

13

12

—

—

3

1

362

Coating, Engraving, Heat Treating, and Allied
Activities

152

52

55

14

35

12

13

12

—

—

2

1

347

226
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)32

Facility HVAC (GWh)18

Process Heating
(GWh)

285

Machine Drive (GWh)

Paint, Coating, and Adhesive Manufacturing

Subsector

Facility Lighting(GWh)

Process Cooling And
Refrigeration (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

2

48

24

6

17

5

—

2

1

341

Grain and Oilseed Milling

143

14

27

1

24

86

6

7

10

7

3

1

329

Other Textile Product Mills

169

22

42

1

34

21

1

8

16

—

8

—

322

Electrical Equipment Manufacturing

78

64

47

22

28

18

10

6

1

—

2

—

277

Lime and Gypsum Product Manufacturing

141

67

17

5

13

10

6

4

2

—

1

0

267

Semiconductor and Other Electronic
Component Manufacturing

60

31

63

6

24

30

19

13

2

6

1

5

258

Ship and Boat Building

90

26

46

2

30

12

12

10

2

4

3

3

241

Sawmills and Wood Preservation

176

15

18

1

14

2

3

4

4

3

1

1

240

Clay Product and Refractory Manufacturing

119

57

15

4

11

9

5

3

2

—

1

0

226

Industrial Machinery Manufacturing

80

18

38

2

26

8

4

7

2

—

2

—

187

Engine, Turbine, and Power Transmission
Equipment Manufacturing

76

17

37

2

25

7

4

7

2

—

2

—

178

Fabric Mills

105

15

27

0

13

7

3

3

4

—

2

—

178

Ventilation, Heating, Air-Conditioning, andCommercial Refrigeration Equipment
Manufacturing

73

17

36

2

24

7

4

6

2

—

2

—

172

Metalworking Machinery Manufacturing

68

16

33

2

23

7

4

6

2

—

2

—

161

Spring and Wire Product Manufacturing

67

23

24

6

15

5

6

5

—

—

1

0

152

Agriculture, Construction, and Mining Machinery
Manufacturing

65

15

31

2

21

6

4

6

1

—

2

—

152

227
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)83

Facility HVAC (GWh)40

Process Heating
(GWh)

113

Machine Drive (GWh)Other Miscellaneous Manufacturing

Subsector

Facility Lighting(GWh)

Process Cooling And
Refrigeration (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

1

19

10

3

7

2

—

1

0

139

Sugar and Confectionery Product
Manufacturing

57

6

11

0

10

35

2

3

4

3

1

1

132

Seafood Product Preparation and Packaging

55

5

11

0

9

34

2

3

4

3

1

1

128

Commercial and Service Industry Machinery
Manufacturing

54

12

26

1

18

5

3

5

1

—

1

—

126

Tobacco Manufacturing

44

5

14

0

11

24

3

4

2

9

2

1

120

Cut and Sew Apparel Manufacturing

40

5

29

—

17

2

—

3

2

—

1

—

100

Computer and Peripheral Equipment
Manufacturing

22

11

24

2

9

12

7

5

1

2

0

2

97

Communications Equipment Manufacturing

19

10

20

2

8

10

6

4

1

2

0

2

82

Machine Shops; Turned Product; and Screw,
Nut, and Bolt Manufacturing

35

12

13

3

8

3

3

3

—

—

1

0

81

Office Furniture (including Fixtures)
Manufacturing

34

3

14

0

12

1

—

2

1

—

1

0

68

Electric Lighting Equipment Manufacturing

19

15

11

5

6

4

2

1

0

—

0

—

66

Textile Furnishings Mills

32

4

8

0

6

4

0

2

3

—

2

—

61

Railroad Rolling Stock Manufacturing

22

6

11

0

7

3

3

2

0

1

1

1

59

Audio and Video Equipment Manufacturing

13

7

14

1

5

7

4

3

1

1

0

1

57

Boiler, Tank, and Shipping Container
Manufacturing

25

9

9

2

6

2

2

2

—

—

0

0

57

228
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)

33

Facility HVAC (GWh)16

Process Heating
(GWh)

46

Machine Drive (GWh)

Medical Equipment and Supplies Manufacturing

Subsector

Facility Lighting(GWh)

Process Cooling And
Refrigeration (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess
Use (GWh)

10

0

7

3

3

2

0

1

1

1

53

Manufacturing and Reproducing Magnetic and
Optical Media

9

5

10

1

4

5

3

2

0

1

0

1

40

Cutlery and Handtool Manufacturing

12

4

4

1

3

1

1

1

—

—

0

0

28

Other Furniture Related Product Manufacturing

7

0

3

0

2

0

—

0

0

—

0

0

13

Other Leather and Allied Product Manufacturing

8

1

1

0

1

0

0

0

0

—

0

—

12

Leather and Hide Tanning and Finishing

1

0

0

0

0

0

0

0

0

—

0

—

2

Footwear Manufacturing

1

0

0

0

0

0

0

0

0

—

0

—

1

45,442 12,010

7,491

7,487

5,293

5,113

1,850

1,538

1,505

1,060

377

Total

Process Heating
(GWh)

229
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Electro Chemical
Processes (GWh)6

Facility HVAC (GWh)20

Machine Drive (GWh)Other Transportation Equipment Manufacturing

Subsector

240 89,405

Figure G-41. Industrial electricity use diurnal patterns by season, modeled for the East South
Central census division 2012

230
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-43. Distributed Generation Model, Annual Summary the East South Central Census
Division in 2012 by Sector and DG Type
Sector/DG Type

Industry

Commercial

Residential

Total

CHP (GWh)

16,553450—

17,002

Thermal DG (GWh)

1,298

—

—

1,298

Distributed PV (GWh)

18

18

6

43

17,869468

618,343

CHP (%)

92.6

96.1

—

92.7

Thermal DG (%)

7.3

—

—

7.1

Distributed PV (%)

0.1

3.9

100.0

0.2

Total (%)

97.4

2.6

0.0

100.0

Total (GWh)

Figure G-42. Distributed Generation Model Diurnal Generation Patterns, East South Central
census division 2012

231
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

G.7 West South Central
Table G-44. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, West South Central Census Division
ComponentType

Component
Name

Top-down

Hourly load

591.1

DerivedT&D losses

32.3

Top-down

Annual energy

dsgridResidential

Commercial

Industrial

Transport

Total

208.2

191.4

158.4

0.1

558.0

Distributed generation

0.0

1.8

58.6

–

60.4

dsgrid-core

Gap models

27.1

57.7

53.9

0.1

138.8

dsgrid-core

Detailed sector models

176.8

138.6

130.5

–

445.9

DerivedTotal site energy

208.2

193.2

217.0

0.1

618.4

DerivedAnnual sector residuals

4.3

-3.1

32.6

-0.0

33.7

DerivedHourly residuals

13.7

Figure G-43. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the West South Central census division in 2012

232
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-44. dsgrid hourly residuals shown in context for the West South Central census division.

Figure G-45. Historical and dsgrid load duration curves for the West South Central census division in2012
The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of thedetailed sector and gap models.

233
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-45. Residential Subsectors, Summary of Electricity by End Use for the West South Central
Census Division in 2012
Interior
Space Interior
Space Water Exterior
Heat
Equipment Cooling Lights Fans Heating Systems Lights Pumps Rejection Total(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
Subsector
SingleFamilyDetached

56,869 54,123 17,698 16,906 13,616

12,823

3,560

3

—

Mobile
Home

6,305

5,076

889

1,416

214

0

— 16,753

Apartment
in Building
2 to 4 Units

2,027

1,320

803

963

168

—

973

76

38

6,369

SingleFamilyAttached

1,495

1,203

312

364

211

336

51

0

—

3,971

MidriseApartment
Building

370

271

158

191

28

—

190

19

9

1,236

67,066 61,993 20,288 19,961 14,911

14,5754,988

99

48 203,928

Total

1,316 1,537

175,598

Table G-46. Residential Electricity Proportions by Subsector and End Use for the West South
Central Census Division in 2012
Interior
Space Interior
Space Water Exterior
Heat
Equipment Cooling Lights Fans Heating Systems Lights Pumps Rejection Total
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
Subsector
Single Family
Detached

32.4

30.8

10.1

9.6

7.8

7.3

2.0

0.0

—

86.1

Mobile Home

37.6

30.3

7.9

9.2

5.3

8.5

1.3

0.0

—

8.2

Apartment in
Building 2 to
4 Units

31.8

20.7

12.6

15.1

2.6

—

15.3

1.2

0.6

3.1

Single Family
Attached

37.6

30.3

7.9

9.2

5.3

8.5

1.3

0.0

—

1.9

MidriseApartment
Building

30.0

21.9

12.8

15.4

2.2

—

15.3

1.6

0.7

0.6

Total

32.9

30.4

9.9

9.8

7.3

7.1

2.4

0.0

0.0 100.0

234This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-46. Residential electricity use diurnal patterns by season, modeled for the West South
Central census division 2012

235
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-47. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the West South Central Census Division in 2012

Subsector

Space
Exterior
Space HeatInterior InteriorLights Equipment Cooling Fans Lights Pumps Heating Rejection Total(GWh) (GWh) (GWh) (GWh) (GWh) (GWh)(GWh)(GWh) (GWh)

Large Office

10,32012,109 10,791

8,549

2,103

1,228

38

638

45,777

Strip Mall

16,687

2,427

3,948

3,408

3,418

14

299

8

30,209Standalone
Retail Store

9,816

3,146

3,155

3,009

1,498

10

432

5

21,072

Small Office

1,712

2,347

1,319

1,255

668

11

64

8

7,383

Medium Office

1,682

1,928

1,573

1,256

599

86

19

40

7,182

Warehouse

2,804

1,117

475

828

1,375

7

347

5

6,959

Full Service
Restaurant

1,233

2,772

960

725

343

9

48

4

6,094

Large Hotel

1,137

1,854

1,359

1,197

282

38

32

17

5,916

Hospital

1,022

1,280

950

801

77

156

—

81

4,368

Primary
School

575

457

344

276

73

15

10

8

1,758

Outpatient
Treatment
Facility

382

566

147

165

138

23

—

13

1,433

Small Hotel

78

117

11

8

61

1

3

1

280

Quick Service
Restaurant

17

99

34

33

10

1

0

0

194

30,218 25,067 21,510

10,645

1,599

1,294

Total

47,463829 138,625

236
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-48. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the West South Central Census Division in 2012

Subsector

SpaceInterior InteriorLights Equipment Cooling Fans
(%)
(%)
(%)
(%)

Exterior
Space Heat
Lights Pumps Heating Rejection Total
(%)
(%)
(%)
(%)
(%)

Large Office

22.5

26.5

23.6

18.7

4.6

2.7

0.1

1.4

33.0

Strip Mall

55.2

8.0

13.1

11.3

11.3

0.0

1.0

0.0

21.8

Standalone
Retail Store

46.6

14.9

15.0

14.3

7.1

0.0

2.0

0.0

15.2

Small Office

23.2

31.8

17.9

17.0

9.0

0.2

0.9

0.1

5.3

Medium Office

23.4

26.8

21.9

17.5

8.3

1.2

0.3

0.6

5.2

Warehouse

40.3

16.0

6.8

11.9

19.8

0.1

5.0

0.1

5.0

Full Service
Restaurant

20.2

45.5

15.7

11.9

5.6

0.2

0.8

0.1

4.4

Large Hotel

19.2

31.3

23.0

20.2

4.8

0.6

0.5

0.3

4.3

Hospital

23.4

29.3

21.8

18.3

1.8

3.6

—

1.9

3.2

Primary
School

32.7

26.0

19.6

15.7

4.2

0.8

0.6

0.4

1.3

Outpatient
Treatment
Facility

26.7

39.5

10.3

11.5

9.6

1.6

—

0.9

1.0

Small Hotel

27.7

41.7

3.9

2.9

21.8

0.4

1.2

0.2

0.2

Quick Service
Restaurant

8.6

50.9

17.6

17.3

5.0

0.3

0.1

0.2

0.1

Total

34.2

21.8

18.1

15.5

7.7

1.2

0.9

0.6

100.0

237
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-47. Commercial electricity use diurnal patterns by season, modeled for the West South
Central census division 2012

238
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Onsite Transportation
(GWh)

151

208

158

237

2

22 21,161

Basic Chemical Manufacturing

10,318

664

3,064

1,150

1,737

741

277

217

384

247

46

46 18,892

Iron and Steel Mills and Ferroalloy
Manufacturing

3,482

4,470

2,974

809

192

568

201

155

61

255

21

36 13,224

Pulp, Paper, and Paperboard Mills

5,965

310

65

346

132

303

266

85

364

39

16

—

7,890

Plastics Product Manufacturing

2,990

895

—

565

447

528

—

127

28

—

24

—

5,602

Converted Paper Product Manufacturing

4,049

211

44

237

90

207

182

58

249

27

11

—

5,366

Resin, Synthetic Rubber, and Artificial Synthetic
Fibers and Filaments Manufacturing

2,403

155

715

268

405

173

65

51

89

58

11

11

4,402

Other Chemical Product and Preparation
Manufacturing

1,878

121

560

210

318

135

51

40

70

45

8

8

3,445

Animal Slaughtering and Processing

1,425

141

7

277

865

244

60

66

101

73

31

14

3,305

Pesticide, Fertilizer, and Other Agricultural
Chemical Manufacturing

1,582

103

486

181

276

116

44

34

61

39

7

7

2,936

Other Wood Product Manufacturing

1,664

141

7

168

16

134

24

36

40

24

6

8

2,266

Steel Product Manufacturing from Purchased
Steel

509

648

416

115

27

81

28

22

9

36

3

5

1,897

Printing and Related Support Activities

993

76

21

312

137

156

26

52

16

—

20

—

1,809

Pharmaceutical and Medicine Manufacturing

970

63

292

109

166

70

26

21

37

24

4

4

1,786

239
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)

End Use Not Reported
(GWh)

535

Other Nonprocess Use(GWh)Conventional Boiler Use
(GWh)

894

Facility Lighting (GWh)804

Process Cooling And
Refrigeration (GWh)307

Facility HVAC (GWh)717Electro Chemical
Processes (GWh)

17,126

Subsector

Process Heating (GWh)Petroleum and Coal Products Manufacturing

Machine Drive (GWh)Other Facility Support(GWh)

Other Process Use (GWh)

Table G-49. Industrial Manufacturing Subsectors, Summary of Model Results for the West South Central Census Division in 2012

Conventional Boiler Use(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use
(GWh)

61

57

—

—

12

3

1,670

Other Fabricated Metal Product Manufacturing

720

246

64

256

53

161

59

55

—

—

11

3

1,629

Other Nonmetallic Mineral Product
Manufacturing

831

394

28

101

60

77

34

24

14

—

6

2

1,571

Semiconductor and Other Electronic
Component Manufacturing

355

185

35

373

177

142

113

75

14

33

4

30

1,535

Nonferrous Metal (except Aluminum)
Production and Processing

402

512

328

90

21

64

22

17

7

28

2

4

1,497

Aerospace Product and Parts Manufacturing

549

156

9

266

72

178

70

56

12

22

20

17

1,427

Soap, Cleaning Compound, and Toilet
Preparation Manufacturing

764

49

234

87

133

56

21

17

29

19

4

3

1,415

Rubber Product Manufacturing

676

202

—

128

102

120

—

29

6

—

5

—

1,268

Agriculture, Construction, and Mining Machinery
Manufacturing

506

117

12

243

48

166

27

44

11

—

12

—

1,186

Cement and Concrete Product Manufacturing

609

289

20

74

45

57

25

18

10

—

4

2

1,152

Foundries

304

390

256

70

17

49

17

13

5

22

2

3

1,149

Alumina and Aluminum Production andProcessing

265

338

217

60

14

42

15

11

4

19

2

3

990

Paint, Coating, and Adhesive Manufacturing

531

34

162

60

92

39

15

12

20

13

2

2

983

Beverage Manufacturing

334

40

2

109

182

86

23

31

15

71

19

4

915

240
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Facility Support(GWh)

Other Process Use (GWh)166

Facility Lighting (GWh)55

Process Cooling And
Refrigeration (GWh)263

Facility HVAC (GWh)

66

Electro Chemical
Processes (GWh)252

Process Heating (GWh)734

Machine Drive (GWh)

Architectural and Structural Metals
Manufacturing

Subsector

Conventional Boiler Use(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use
(GWh)

9

14

16

10

2

3

908

Fruit and Vegetable Preserving and Specialty
Food Manufacturing

387

38

2

75

235

66

16

18

28

20

8

4

898

Motor Vehicle Manufacturing

307

88

5

151

41

101

40

32

7

13

11

10

806

Other Food Manufacturing

342

34

2

66

205

58

14

16

24

17

7

3

789

Grain and Oilseed Milling

321

32

2

62

192

54

13

15

23

16

7

3

739

Bakeries and Tortilla Manufacturing

319

32

2

62

192

54

13

15

23

16

7

3

736

Glass and Glass Product Manufacturing

384

182

13

46

27

35

15

11

6

—

3

1

723

Other General Purpose Machinery
Manufacturing

282

65

7

138

27

94

16

25

6

—

7

—

669

Coating, Engraving, Heat Treating, and Allied
Activities

291

100

26

106

22

66

25

23

—

—

5

1

665

Motor Vehicle Parts Manufacturing

252

72

4

123

33

82

32

26

5

10

9

8

657

Other Electrical Equipment and Component
Manufacturing

174

143

48

103

40

61

23

14

3

—

4

—

613

Dairy Product Manufacturing

263

26

1

50

156

44

11

12

18

13

6

2

603

Animal Food Manufacturing

251

25

1

49

155

43

11

12

18

13

5

2

586

Other Miscellaneous Manufacturing

191

67

4

141

42

81

11

28

9

—

3

1

580

241
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Facility Support(GWh)

Other Process Use (GWh)53

Facility Lighting (GWh)6

Process Cooling And
Refrigeration (GWh)66

Facility HVAC (GWh)3Electro Chemical
Processes (GWh)57

Process Heating (GWh)669

Machine Drive (GWh)

Veneer, Plywood, and Engineered Wood
Product Manufacturing

Subsector

Conventional Boiler Use(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use
(GWh)

41

27

5

12

1

11

554

Electrical Equipment Manufacturing

146

120

42

88

34

52

19

12

2

—

4

—

519

Lime and Gypsum Product Manufacturing

269

128

9

33

20

25

11

8

4

—

2

1

508

Other Textile Product Mills

238

32

1

59

30

48

2

11

22

—

11

—

454

Communications Equipment Manufacturing

86

45

9

92

44

35

28

19

3

8

1

7

377

Motor Vehicle Body and Trailer Manufacturing

129

37

2

63

17

42

17

13

3

5

5

4

337

Commercial and Service Industry Machinery
Manufacturing

139

32

3

67

13

46

8

12

3

—

3

—

328

Railroad Rolling Stock Manufacturing

123

35

2

61

17

41

16

13

3

5

5

4

324

Household and Institutional Furniture andKitchen Cabinet Manufacturing

160

12

1

66

5

54

—

11

4

—

4

2

319

Ship and Boat Building

118

34

2

60

16

40

16

13

3

5

5

4

315

Spring and Wire Product Manufacturing

124

43

11

45

9

28

10

10

—

—

2

1

282

Engine, Turbine, and Power Transmission
Equipment Manufacturing

116

27

3

56

11

38

6

10

3

—

3

—

272

Industrial Machinery Manufacturing

115

26

3

55

11

38

6

10

3

—

3

—

270

Forging and Stamping

115

39

10

41

9

26

10

9

—

—

2

1

261

Computer and Peripheral Equipment
Manufacturing

55

29

6

61

29

23

19

12

2

6

1

5

248

242
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Facility Support(GWh)

Other Process Use (GWh)51

Facility Lighting (GWh)65

Process Cooling And
Refrigeration (GWh)135

Facility HVAC (GWh)13Electro Chemical
Processes (GWh)66

Process Heating (GWh)126

Machine Drive (GWh)Navigational, Measuring, Electromedical, andControl Instruments Manufacturing

Subsector

Conventional Boiler Use(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use
(GWh)

18

4

4

5

—

2

—

241

Sugar and Confectionery Product
Manufacturing

103

10

1

20

63

18

4

5

7

5

2

1

240

Ventilation, Heating, Air-Conditioning, andCommercial Refrigeration Equipment
Manufacturing

93

22

2

46

9

31

5

8

2

—

2

—

222

Clay Product and Refractory Manufacturing

117

56

4

14

9

11

5

3

2

—

1

0

222

Medical Equipment and Supplies Manufacturing

72

25

2

52

15

30

4

10

3

—

1

1

216

Cut and Sew Apparel Manufacturing

79

10

—

57

3

33

—

7

4

—

1

—

195

Textile and Fabric Finishing and Fabric Coating
Mills

104

15

0

26

7

13

3

3

4

—

2

—

177

Seafood Product Preparation and Packaging

71

7

0

14

44

12

3

3

5

4

2

1

165

Metalworking Machinery Manufacturing

54

13

1

26

5

18

3

5

1

—

1

—

128

Boiler, Tank, and Shipping Container
Manufacturing

55

19

5

19

4

12

4

4

—

—

1

0

124

Machine Shops; Turned Product; and Screw,
Nut, and Bolt Manufacturing

54

19

5

20

4

12

5

4

—

—

1

0

124

Fiber, Yarn, and Thread Mills

61

9

0

15

4

7

2

2

2

—

1

—

103

Office Furniture (including Fixtures)
Manufacturing

45

3

0

19

2

15

—

3

1

—

1

0

89

Other Transportation Equipment Manufacturing

34

10

1

17

4

11

4

3

1

1

1

1

88

Electro Chemical
Processes (GWh)243
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Facility Support(GWh)

Other Process Use (GWh)9Facility Lighting (GWh)36

Process Cooling And
Refrigeration (GWh)0

Facility HVAC (GWh)21

Process Heating (GWh)143

Machine Drive (GWh)Fabric Mills

Subsector

Conventional Boiler Use(GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use
(GWh)

5

1

4

1

1

1

1

0

0

73

Electric Lighting Equipment Manufacturing

20

16

5

12

5

7

3

2

0

—

0

—

70

Household Appliance Manufacturing

19

16

5

11

4

7

3

2

0

—

0

—

68

Cutlery and Handtool Manufacturing

19

6

2

7

1

4

2

1

—

—

0

0

43

Other Leather and Allied Product Manufacturing

21

3

0

3

1

4

0

1

0

—

0

—

33

Tobacco Manufacturing

12

1

0

4

6

3

1

1

1

2

1

0

32

Audio and Video Equipment Manufacturing

7

3

1

7

3

3

2

1

0

1

0

1

28

Other Furniture Related Product Manufacturing

14

1

0

6

0

5

—

1

0

—

0

0

27

Textile Furnishings Mills

11

1

0

3

1

2

0

1

1

—

1

—

22

Footwear Manufacturing

8

1

0

1

0

1

0

0

0

—

0

—

12

Manufacturing and Reproducing Magnetic and
Optical Media

2

1

0

2

1

1

1

0

0

0

0

0

9

Apparel Accessories and Other Apparel
Manufacturing

2

0

—

1

0

1

—

0

0

—

0

—

5

Leather and Hide Tanning and Finishing

2

0

0

0

0

0

0

0

0

—

0

—

3

Hardware Manufacturing

1

0

0

0

0

0

0

0

—

—

0

0

2

70,703 13,876 10,661 10,395

8,684

7,160

2,422

2,155

2,102

1,516

472

Total

Facility HVAC (GWh)Electro Chemical
Processes (GWh)

Machine Drive (GWh)244This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Facility Support(GWh)

Other Process Use (GWh)0

Facility Lighting (GWh)5

Process Cooling And
Refrigeration (GWh)54

Process Heating (GWh)

Sawmills and Wood Preservation

Subsector

324 130,471

Figure G-48. Industrial electricity use diurnal patterns by season, modeled for the West South
Central census division 2012

245
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-50. Distributed Generation Model, Annual Summary the West South Central Census
Division in 2012 by Sector and DG Type
Sector/DG Type

Industry

Commercial

Residential

Total

CHP (GWh)

55,8611,787

057,648

Thermal DG (GWh)

2,692

35

—

2,727

Distributed PV (GWh)

15

14

30

60

58,5681,837

30

60,435

CHP (%)

95.4

97.3

0.2

95.4

Thermal DG (%)

4.6

1.9

—

4.5

Distributed PV (%)

0.0

0.8

99.8

0.1

Total (%)

96.9

3.0

0.1

100.0

Total (GWh)

Figure G-49. Distributed Generation Model Diurnal Generation Patterns, West South Central
census division 2012

246
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G.8 Mountain
Table G-51. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, Mountain Census Division
ComponentType

Component
Name

Top-down

Hourly load

289.4

DerivedT&D losses

15.0

Top-down

Annual energy

dsgridDistributed generation

dsgrid-core

Residential

Commercial

Industrial

Transport

Total

94.9

98.2

82.3

0.1

275.4

–

1.0

3.6

–

4.5

Gap models

12.4

27.7

22.2

0.1

62.4

dsgrid-core

Detailed sector models

84.4

65.3

36.5

–

186.1

DerivedTotal site energy

94.9

99.1

85.9

0.1

280.0

DerivedAnnual sector residuals

-1.9

6.2

27.2

-0.0

31.5

DerivedHourly residuals

29.1

Figure G-50. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the Mountain census division in 2012

247
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-51. dsgrid hourly residuals shown in context for the Mountain census division.

Figure G-52. Historical and dsgrid load duration curves for the Mountain census division in 2012
The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of thedetailed sector and gap models.

248
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Table G-52. Residential Subsectors, Summary of Electricity by End Use for the Mountain Census Division in 2012
Interior
Equipment
(GWh)

Space
Cooling(GWh)

Interior
Lights
(GWh)

Fans
(GWh)

Space
Heating
(GWh)

Water
Systems(GWh)

Exterior
Lights
(GWh)

Pumps
(GWh)

Single Family
Detached

32,88015,722

10,540

8,833

7,934

5,447

2,136

58

—

83,551

Mobile Home

2,988

1,046

628

643

502

522

102

4

—

6,435

Apartment in Building
2 to 4 Units

1,009

397

430

519

156

—

514

24

5

3,053

Single Family
Attached

1,332

466

280

287

224

233

45

2

—

2,868

274

95

123

140

40

—

146

8

2

827

38,482

17,727

12,002

10,422

8,856

6,201

2,944

95

6

96,735Subsector

Midrise Apartment
Building
Total

Heat
Rejection
(GWh)

Total
(GWh)

Table G-53. Residential Electricity Proportions by Subsector and End Use for the Mountain Census Division in 2012
Interior
Equipment(%)

Subsector

Space
Cooling(%)

SpaceInterior
HeatingLightsFans (%)
(%)
(%)

Water
Systems(%)aExterior Pumps
Lights (%)
(%)

Heat
Rejection(%)

Total (%)

Single Family Detached

39.4

18.8

12.6

10.6

9.5

6.5

2.6

0.1

—

86.4

Mobile Home

46.4

16.3

9.8

10.0

7.8

8.1

1.6

0.1

—

6.7

Apartment in Building
2 to 4 Units

33.0

13.0

14.1

17.0

5.1

—

16.8

0.8

0.2

3.2

Single Family Attached

46.4

16.3

9.8

10.0

7.8

8.1

1.6

0.1

—

3.0

Midrise Apartment
Building

33.1

11.5

14.8

17.0

4.8

—

17.7

0.9

0.2

0.9

Total

39.8

18.3

12.4

10.8

9.2

6.4

3.0

0.1

0.0

100.0

249
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-53. Residential electricity use diurnal patterns by season, modeled for the Mountaincensus division 2012250
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-54. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the Mountain Census Division in 2012

Subsector

Space Exterior Space
Heat
Interior InteriorLights Equipment Fans Cooling Lights Heating Pumps Rejection Total(GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
(GWh)
(GWh)

Strip Mall

9,437

1,379

2,464

1,231

1,927

399

10

2

16,849Large Office

3,463

4,073

3,177

1,874

726

62

284

67

13,726

Standalone
Retail Store

4,309

1,421

1,715

861

665

544

1

0

9,514

Medium Office

1,357

1,546

1,203

741

482

37

45

11

5,423

Warehouse

1,634

651

716

199

804

630

3

1

4,638

Small Office

1,114

1,523

872

557

435

115

5

2

4,623

Full Service
Restaurant

724

1,616

447

310

202

70

3

1

3,372

Large Hotel

648

1,024

718

445

163

60

31

6

3,094

Hospital

460

704

283

189

38

—

32

9

1,714

Primary
School

330

266

177

118

43

18

5

1

958

Outpatient
Treatment
Facility

261

381

123

60

94

—

11

3

933

Small Hotel

97

142

9

4

71

15

1

0

339

Quick Service
Restaurant

6

36

13

8

4

0

—

—

67

14,761 11,918

6,596

5,653

1,949

429

105

65,251

Total

23,839251This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-55. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the Mountain Census Division in 2012

Subsector

Space Exterior Space
Heat
Interior InteriorLights Equipment Fans Cooling Lights Heating Pumps Rejection
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)

Total
(%)

Strip Mall

56.0

8.2

14.6

7.3

11.4

2.4

0.1

0.0

25.8

Large Office

25.2

29.7

23.1

13.7

5.3

0.5

2.1

0.5

21.0

Standalone
Retail Store

45.3

14.9

18.0

9.0

7.0

5.7

0.0

0.0

14.6

Medium Office

25.0

28.5

22.2

13.7

8.9

0.7

0.8

0.2

8.3

Warehouse

35.2

14.0

15.4

4.3

17.3

13.6

0.1

0.0

7.1

Small Office

24.1

33.0

18.9

12.1

9.4

2.5

0.1

0.0

7.1

Full Service
Restaurant

21.5

47.9

13.3

9.2

6.0

2.1

0.1

0.0

5.2

Large Hotel

20.9

33.1

23.2

14.4

5.3

1.9

1.0

0.2

4.7

Hospital

26.8

41.0

16.5

11.0

2.2

—

1.9

0.5

2.6

Primary
School

34.4

27.8

18.5

12.3

4.5

1.9

0.5

0.2

1.5

Outpatient
Treatment
Facility

28.0

40.8

13.2

6.4

10.1

—

1.1

0.4

1.4

Small Hotel

28.5

41.9

2.6

1.3

20.9

4.6

0.2

0.0

0.5

Quick Service
Restaurant

9.0

53.4

19.9

12.1

5.3

0.3

—

—

0.1

Total

36.5

22.6

18.3

10.1

8.7

3.0

0.7

0.2

100.0

252
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-54. Commercial electricity use diurnal patterns by season, modeled for the Mountaincensus division 2012

253
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Process Heating (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use
(GWh)

3,212

134

151

168

100

58

28

39

30

45

0

4

1,435

429

271

214

253

—

—

61

13

—

11

—

697

894

162

38

114

595

40

31

12

51

4

7

1,602

83

93

35

81

17

71

23

98

11

4

—

1,161

60

68

26

59

13

52

17

71

8

3

—

313

163

328

156

125

31

100

66

12

29

4

26

892

76

90

9

72

4

13

19

21

13

3

4

644

41

72

108

46

191

17

14

24

15

3

3

Pharmaceutical and Medicine
Manufacturing

620

40

70

106

45

187

17

13

23

15

3

3

Other Nonmetallic Mineral Product
Manufacturing

533

253

65

39

49

18

22

15

9

—

4

2

Dairy Product Manufacturing

421

42

81

250

71

2

17

19

29

21

9

4

Subsector
Petroleum and Coal Products
Manufacturing
Plastics Product Manufacturing
Iron and Steel Mills and Ferroalloy
ManufacturingPulp, Paper, and Paperboard Mills
Converted Paper Product Manufacturing
Semiconductor and Other Electronic
Component Manufacturing
Other Wood Product Manufacturing
Basic Chemical Manufacturing

254
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)

Machine Drive (GWh)

Table G-56. Industrial Manufacturing Subsectors, Summary of Model Results for the Mountain Census Division in 2012

3,969
2,689
2,645
2,119
1,539
1,352
1,216
1,179
1,142
1,008
967

Machine Drive (GWh)

Process Heating (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use(GWh)

Total (GWh)

Aerospace Product and Parts
Manufacturing

361

103

175

47

117

6

46

37

8

15

13

11

938

Nonferrous Metal (except Aluminum)
Production and Processing

250

319

56

13

40

204

14

11

4

17

1

2

932

Navigational, Measuring, Electromedical,
and Control Instruments Manufacturing

205

107

219

105

83

21

67

45

8

20

2

17

898

Pesticide, Fertilizer, and Other Agricultural
Chemical Manufacturing

419

27

48

73

31

129

12

9

16

10

2

2

778

Animal Slaughtering and Processing

325

32

63

197

56

2

14

15

23

17

7

3

753

Beverage Manufacturing

246

29

81

135

64

1

17

23

11

52

14

3

675

Cement and Concrete Product
Manufacturing

356

169

43

26

33

12

14

10

6

—

3

1

675

Architectural and Structural Metals
Manufacturing

281

96

101

21

63

25

23

22

—

—

4

1

639

Communications Equipment Manufacturing

132

69

142

68

54

13

44

29

5

13

2

11

583

Fruit and Vegetable Preserving andSpecialty Food Manufacturing

242

24

47

147

41

1

10

11

17

12

5

2

561

Other Fabricated Metal Product
Manufacturing

246

84

87

18

55

22

20

19

—

—

4

1

556

Glass and Glass Product Manufacturing

273

129

33

20

25

9

11

8

4

—

2

1

514

Computer and Peripheral Equipment
Manufacturing

114

60

125

61

47

12

39

26

5

11

1

10

511

Subsector

255
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Machine Drive (GWh)

Process Heating (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use(GWh)

Total (GWh)

Soap, Cleaning Compound, and Toilet
Preparation Manufacturing

267

17

30

46

20

82

7

6

10

7

1

1

495

Other Chemical Product and Preparation
Manufacturing

269

17

30

46

19

80

7

6

10

6

1

1

494

Veneer, Plywood, and Engineered Wood
Product Manufacturing

361

31

36

3

29

1

5

8

8

5

1

2

489

Other Miscellaneous Manufacturing

144

51

106

31

61

3

8

21

7

—

3

1

438

Printing and Related Support Activities

230

18

72

32

36

5

6

12

4

—

5

—

419

Bakeries and Tortilla Manufacturing

178

18

34

107

30

1

7

8

13

9

4

2

411

Animal Food Manufacturing

156

15

31

96

27

1

7

7

11

8

3

2

363

Other Food Manufacturing

136

13

26

81

23

1

6

6

10

7

3

1

313

Foundries

80

103

18

4

13

67

5

4

1

6

0

1

302

Coating, Engraving, Heat Treating, andAllied Activities

114

39

41

9

26

10

10

9

—

—

2

1

260

Grain and Oilseed Milling

109

11

21

65

19

1

5

5

8

6

2

1

251

Household and Institutional Furniture andKitchen Cabinet Manufacturing

117

9

48

4

40

1

—

8

3

—

3

1

232

Motor Vehicle Parts Manufacturing

78

22

38

10

25

1

10

8

2

3

3

2

202

Alumina and Aluminum Production andProcessing

52

66

12

3

8

43

3

2

1

4

0

1

194

Subsector

256
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Process Heating (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use(GWh)

Total (GWh)

63

22

46

14

27

1

4

9

3

—

1

0

190

Paint, Coating, and Adhesive
Manufacturing

91

6

10

16

7

28

3

2

3

2

0

0

168

Lime and Gypsum Product Manufacturing

83

39

10

6

8

3

3

2

1

—

1

0

156

Sugar and Confectionery Product
Manufacturing

66

7

13

41

11

0

3

3

5

3

1

1

154

Commercial and Service Industry
Machinery Manufacturing

65

15

32

6

22

2

4

6

1

—

2

—

154

Other General Purpose Machinery
Manufacturing

57

13

28

6

19

1

3

5

1

—

1

—

134

Other Electrical Equipment and Component
Manufacturing

37

30

22

8

13

10

5

3

1

—

1

—

129

Motor Vehicle Body and Trailer
Manufacturing

48

14

23

6

16

1

6

5

1

2

2

1

124

Steel Product Manufacturing fromPurchased Steel

33

42

7

2

5

27

2

1

1

2

0

0

123

Agriculture, Construction, and Mining
Machinery Manufacturing

51

12

25

5

17

1

3

4

1

—

1

—

119

Rubber Product Manufacturing

56

17

11

8

10

—

—

2

1

—

0

—

105

Resin, Synthetic Rubber, and Artificial
Synthetic Fibers and Filaments
Manufacturing

54

3

6

9

4

16

1

1

2

1

0

0

100

Machine Drive (GWh)

Medical Equipment and Supplies
Manufacturing

Subsector

257This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Process Heating (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use(GWh)

Total (GWh)

38

9

18

4

13

1

2

3

1

—

1

—

90

Other Textile Product Mills

46

6

11

6

9

0

0

2

4

—

2

—

87

Electrical Equipment Manufacturing

23

19

14

5

8

7

3

2

0

—

1

—

83

Clay Product and Refractory Manufacturing

40

19

5

3

4

1

2

1

1

—

0

0

75

Audio and Video Equipment Manufacturing

17

9

18

9

7

2

6

4

1

2

0

1

75

Forging and Stamping

30

10

11

2

7

3

2

2

—

—

0

0

67

Manufacturing and Reproducing Magnetic
and Optical Media

11

6

12

6

5

1

4

2

0

1

0

1

49

Engine, Turbine, and Power Transmission
Equipment Manufacturing

20

5

10

2

7

0

1

2

0

—

0

—

48

Machine Shops; Turned Product; andScrew, Nut, and Bolt Manufacturing

21

7

8

2

5

2

2

2

—

—

0

0

47

Other Transportation Equipment
Manufacturing

18

5

9

2

6

0

2

2

0

1

1

1

47

Metalworking Machinery Manufacturing

19

4

9

2

6

0

1

2

0

—

0

—

46

Spring and Wire Product Manufacturing

18

6

7

1

4

2

2

1

—

—

0

0

41

Seafood Product Preparation andPackaging

16

2

3

10

3

0

1

1

1

1

0

0

38

Tobacco Manufacturing

10

1

3

6

3

0

1

1

0

2

1

0

28

Boiler, Tank, and Shipping Container
Manufacturing

13

4

4

1

3

1

1

1

—

—

0

0

28

Machine Drive (GWh)

Industrial Machinery Manufacturing

Subsector

258This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Process Heating (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use(GWh)

Total (GWh)

16

2

4

1

2

0

0

0

1

—

0

—

27

Railroad Rolling Stock Manufacturing

10

3

5

1

3

0

1

1

0

0

0

0

25

Sawmills and Wood Preservation

17

1

2

0

1

0

0

0

0

0

0

0

23

Other Furniture Related Product
Manufacturing

11

1

4

0

4

0

—

1

0

—

0

0

22

Ventilation, Heating, Air-Conditioning, andCommercial Refrigeration Equipment
Manufacturing

7

2

4

1

3

0

0

1

0

—

0

—

18

Ship and Boat Building

7

2

3

1

2

0

1

1

0

0

0

0

18

Office Furniture (including Fixtures)
Manufacturing

9

1

4

0

3

0

—

1

0

—

0

0

17

Electric Lighting Equipment Manufacturing

5

4

3

1

2

1

1

0

0

—

0

—

17

Cutlery and Handtool Manufacturing

7

3

3

1

2

1

1

1

—

—

0

0

17

Household Appliance Manufacturing

4

3

2

1

1

1

0

0

0

—

0

—

13

Motor Vehicle Manufacturing

5

1

2

1

2

0

1

0

0

0

0

0

12

Cut and Sew Apparel Manufacturing

5

1

3

0

2

—

—

0

0

—

0

—

12

Fabric Mills

7

1

2

0

1

0

0

0

0

—

0

—

11

Textile and Fabric Finishing and Fabric
Coating Mills

4

1

1

0

1

0

0

0

0

—

0

—

7

Other Leather and Allied Product
Manufacturing

4

0

1

0

1

0

0

0

0

—

0

—

6

Machine Drive (GWh)Fiber, Yarn, and Thread Mills

Subsector

259This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Process Heating (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)Other Process Use(GWh)

Other Facility Support(GWh)

Conventional Boiler
Use (GWh)

End Use Not Reported(GWh)

Onsite Transportation(GWh)

Other Nonprocess Use(GWh)

Total (GWh)

1

0

0

0

0

0

0

0

0

—

0

—

2

Leather and Hide Tanning and Finishing

1

0

0

0

0

0

0

0

0

—

0

—

1

Footwear Manufacturing

1

0

0

0

0

0

0

0

0

—

0

—

1

Apparel Accessories and Other Apparel
Manufacturing

0

0

0

0

0

—

—

0

0

—

0

—

0

18,435 4,253 3,631 2,814 2,375 1,983

864

770

572

455

162

144

36,45
9

Machine Drive (GWh)

Textile Furnishings Mills

Subsector

Total

260
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-55. Industrial electricity use diurnal patterns by season, modeled for the Mountaincensus division 2012

261
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-57. Distributed Generation Model, Annual Summary the Mountain Census Division in 2012by Sector and DG Type
Sector/DG TypeCHP (GWh)

Industry

Commercial

Residential

Total

3,080

669

—

3,749

Distributed PV (GWh)

247

246

445

938

Thermal DG (GWh)

247

38

—

285

Total (GWh)

3,574

953

445

4,972

CHP (%)

86.2

70.2

—

75.4

Distributed PV (%)

6.9

25.9

100.0

18.9

Thermal DG (%)

6.9

4.0

—

5.7

Total (%)

71.9

19.2

8.9

100.0

Figure G-56. Distributed Generation Model Diurnal Generation Patterns, Mountain census division 2012

262
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G.9 Pacific
Table G-58. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in
dsgrid, Pacific Census Division
ComponentType

Component
Name

Top-down

Hourly load

418.7

DerivedT&D losses

21.9

Top-down

Annual energy

dsgridResidential

Commercial

Industrial

Transport

Total

144.5

179.6

86.5

0.7

411.3

Distributed generation

1.4

6.5

30.0

–

37.9

dsgrid-core

Gap models

23.0

77.2

24.2

0.7

125.1

dsgrid-core

Detailed sector models

137.1

163.4

83.3

–

383.8

DerivedTotal site energy

145.9

186.1

116.5

0.7

449.2

DerivedAnnual sector residuals

-14.2

-54.5

9.0

0.0

-59.7

DerivedHourly residuals-74.2

Figure G-57. Bottom-up detailed sectoral and gap model load compared to bulk-level historicalhourly load plus DG estimates for the Pacific census division in 2012

263
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-58. dsgrid hourly residuals shown in context for the Pacific census division.

Figure G-59. Historical and dsgrid load duration curves for the Pacific census division in 2012
The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of thedetailed sector and gap models.

264
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-59. Residential Subsectors, Summary of Electricity by End Use for the PacificCensus Division in 2012
Interior
Space Interior Space
Water Exterior
Heat
Equipment Heating Lights Cooling Fans Systems Lights Pumps Rejection Total(GWh)(GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
Subsector
SingleFamilyDetached

54,567 21,167 17,893 16,840 11,922

Mobile
Home

3,880

803

876

1,001

SingleFamilyAttached

3,783

783

854

976

Apartment
in Building
2 to 4 Units

2,673

378

1,127

MidriseApartment
Building

957

127

419

Total

8,438

3,592

9

—

673

480

143

0

—

7,855

656

468

139

0

—

7,659

831 1,012

—

1,352

95

19

7,487

267

344

—

500

32

6

2,652

65,861 23,257 21,168 19,915 14,606

9,386

5,725

137

26 160,080

134,428

Table G-60. Residential Electricity Proportions by Subsector and End Use for the Pacific census
division in 2012

Subsector

Interior
Space Interior Space
Water Exterior
Heat
Equipment Heating Lights Cooling Fans Systems Lights Pumps Rejection Total
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)

Single Family
Detached

40.6

15.7

13.3

12.5

8.9

6.3

2.7

0.0

—

84.0

Mobile Home

49.4

10.2

11.1

12.7

8.6

6.1

1.8

0.0

—

4.9

Single Family
Attached

49.4

10.2

11.1

12.7

8.6

6.1

1.8

0.0

—

4.8

Apartment in
Building 2 to
4 Units

35.7

5.1

15.0

11.1 13.5

—

18.1

1.3

0.3

4.7

MidriseApartment
Building

36.1

4.8

15.8

10.1 13.0

—

18.8

1.2

0.2

1.7

Total

41.1

14.5

13.2

12.4

5.9

3.6

0.1

0.0 100.0

9.1

265This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure G-60. Residential electricity use diurnal patterns by season, modeled for the Pacificcensus division 2012

266
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-61. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End
Use for the Pacific Census Division in 2012

Subsector

Space Exterior Space
Heat
Interior InteriorLights Equipment Fans Cooling Lights Heating Pumps Rejection Total(GWh) (GWh) (GWh) (GWh) (GWh) (GWh) (GWh)
(GWh)
(GWh)

Large Office

14,163

16,430

9,870

6,847

2,900

105

1,148

239

51,703

Strip Mall

24,987

3,628

4,582

2,139

5,105

546

14

4

41,006

Standalone
Retail Store

9,557

3,110

2,808

1,348

1,462

787

5

1

19,077

Medium Office

3,487

3,971

2,301

1,422

1,241

77

114

25

12,638Warehouse

3,888

1,550

970

281

1,882

702

5

2

9,279

Small Office

2,187

2,976

1,477

867

854

162

11

4

8,537

Large Hotel

1,516

2,334

1,240

917

368

33

98

19

6,526

Full Service
Restaurant

1,378

3,077

785

402

385

82

7

1

6,117

Hospital

975

1,274

621

456

75

—

86

22

3,508

Outpatient
Treatment
Facility

687

1,017

258

110

243

—

22

6

2,343

Primary
School

648

519

304

195

84

53

12

3

1,818

Small Hotel

227

307

32

17

144

13

3

1

743

Quick Service
Restaurant

10

57

19

9

6

0

0

0

101

40,249 25,265 15,011

14,748

2,561

1,524

Total

63,709328 163,396

267
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-62. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for
the Pacific Census Division in 2012

Subsector

Space Exterior Space
Heat
Interior InteriorLights Equipment Fans Cooling Lights Heating Pumps Rejection
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)

Total
(%)

Large Office

27.4

31.8

19.1

13.2

5.6

0.2

2.2

0.5

31.6

Strip Mall

60.9

8.8

11.2

5.2

12.5

1.3

0.0

0.0

25.1

Standalone
Retail Store

50.1

16.3

14.7

7.1

7.7

4.1

0.0

0.0

11.7

Medium Office

27.6

31.4

18.2

11.3

9.8

0.6

0.9

0.2

7.7

Warehouse

41.9

16.7

10.4

3.0

20.3

7.6

0.1

0.0

5.7

Small Office

25.6

34.9

17.3

10.2

10.0

1.9

0.1

0.1

5.2

Large Hotel

23.2

35.8

19.0

14.1

5.6

0.5

1.5

0.3

4.0

Full Service
Restaurant

22.5

50.3

12.8

6.6

6.3

1.3

0.1

0.0

3.7

Hospital

27.8

36.3

17.7

13.0

2.1

—

2.4

0.6

2.1

Outpatient
Treatment
Facility

29.3

43.4

11.0

4.7

10.4

—

1.0

0.3

1.4

Primary
School

35.7

28.5

16.7

10.7

4.6

2.9

0.7

0.2

1.1

Small Hotel

30.5

41.3

4.2

2.3

19.4

1.8

0.4

0.1

0.5

Quick Service
Restaurant

9.5

56.3

19.2

9.0

5.7

0.2

0.0

0.0

0.1

Total

39.0

24.6

15.5

9.2

9.0

1.6

0.9

0.2

100.0

268
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Figure G-61. Commercial electricity use diurnal patterns by season, modeled for the Pacificcensus division 2012

269
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Machine Drive (GWh)

Process Heating (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)Other Process Use (GWh)Other Facility Support(GWh)

Conventional Boiler Use(GWh)

End Use Not Reported(GWh)

Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

Total (GWh)

Table G-63. Industrial Manufacturing Subsectors, Summary of Model Results for the Pacific Census Division in 2012

Petroleum and Coal Products Manufacturing

6,454

270

303

337

202

116

57

78

59

89

8

1

7,974

Plastics Product Manufacturing

3,389

1,014

640

506

598

—

—

144

32

—

—

27

6,350

Iron and Steel Mills and Ferroalloy
Manufacturing

1,628

2,090

378

90

266

1,390

94

73

28

119

17

10

6,184

Semiconductor and Other Electronic
Component Manufacturing

1,387

722

1,457

693

555

137

442

295

53

131

116

16

6,003

Converted Paper Product Manufacturing

3,723

194

218

83

190

41

168

53

229

25

—

10

4,934

Pulp, Paper, and Paperboard Mills

3,702

192

215

82

188

40

165

53

226

24

—

10

4,896

Pharmaceutical and Medicine Manufacturing

1,780

115

200

304

129

536

49

38

67

43

8

8

3,276

Aerospace Product and Parts Manufacturing

980

278

475

128

318

16

124

100

21

40

30

36

2,547

Navigational, Measuring, Electromedical, andControl Instruments Manufacturing

578

302

617

295

235

58

189

126

22

56

49

7

2,534

Beverage Manufacturing

887

105

290

485

230

5

61

81

40

188

11

50

2,433

Fruit and Vegetable Preserving and Specialty
Food Manufacturing

942

93

183

572

161

5

40

44

67

48

9

20

2,185

Other Wood Product Manufacturing

1,309

111

132

13

106

5

19

28

31

19

6

4

1,783

Other Food Manufacturing

701

69

135

420

119

4

29

32

49

35

7

15

1,615

Dairy Product Manufacturing

680

67

130

404

115

3

28

31

47

34

6

14

1,561

Communications Equipment Manufacturing

320

167

344

165

131

33

105

70

13

31

28

4

1,410

Basic Chemical Manufacturing

693

45

77

117

50

206

19

15

26

17

3

3

1,270

Foundries

327

419

75

18

53

276

19

14

6

24

3

2

1,235

Subsector

270
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Machine Drive (GWh)

Process Heating (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)Other Process Use (GWh)Other Facility Support(GWh)

Conventional Boiler Use(GWh)

End Use Not Reported(GWh)

Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

Total (GWh)

Veneer, Plywood, and Engineered Wood
Product Manufacturing

875

74

87

8

69

3

12

18

20

12

4

3

1,187

Animal Slaughtering and Processing

497

49

97

302

85

3

21

23

35

25

5

11

1,152

Computer and Peripheral Equipment
Manufacturing

256

135

281

136

106

27

87

58

10

26

23

3

1,146

Glass and Glass Product Manufacturing

560

265

67

40

51

18

22

16

9

—

2

4

1,055

Architectural and Structural Metals
Manufacturing

463

159

166

35

104

41

39

36

—

—

2

7

1,053

Other Nonmetallic Mineral Product
Manufacturing

532

252

64

39

49

18

21

15

9

—

2

4

1,005

Nonferrous Metal (except Aluminum)
Production and Processing

265

338

60

14

42

216

15

11

4

19

3

2

987

Bakeries and Tortilla Manufacturing

416

41

80

251

71

2

17

19

29

21

4

9

961

Soap, Cleaning Compound, and Toilet
Preparation Manufacturing

474

31

54

82

35

145

13

10

18

12

2

2

879

Pesticide, Fertilizer, and Other Agricultural
Chemical Manufacturing

468

30

53

82

34

144

13

10

18

12

2

2

868

Manufacturing and Reproducing Magnetic and
Optical Media

191

99

201

96

77

19

61

41

7

18

16

2

828

Other Fabricated Metal Product Manufacturing

337

115

120

25

75

30

28

26

—

—

2

5

763

Printing and Related Support Activities

405

31

127

56

64

9

11

21

6

—

—

8

738

Coating, Engraving, Heat Treating, and Allied
Activities

317

109

115

24

72

29

27

25

—

—

2

5

725

Subsector

271
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Machine Drive (GWh)

Process Heating (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)Other Process Use (GWh)Other Facility Support(GWh)

Conventional Boiler Use(GWh)

End Use Not Reported(GWh)

Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

Total (GWh)Other Chemical Product and Preparation
Manufacturing

368

24

41

62

26

110

10

8

14

9

2

2

674

Cement and Concrete Product Manufacturing

329

156

40

24

31

11

13

10

6

—

1

2

623

Resin, Synthetic Rubber, and Artificial Synthetic
311
Fibers and Filaments Manufacturing

20

35

52

22

92

8

7

12

7

1

1

570

Alumina and Aluminum Production andProcessing

147

187

33

8

23

120

8

6

2

10

1

1

548

Other Miscellaneous Manufacturing

175

62

129

38

74

4

10

26

9

—

1

3

531

Paint, Coating, and Adhesive Manufacturing

260

17

30

45

19

79

7

6

10

6

1

1

482

Grain and Oilseed Milling

193

19

37

116

33

1

8

9

14

10

2

4

445

Motor Vehicle Parts Manufacturing

167

47

81

22

54

3

21

17

4

7

5

6

435

Rubber Product Manufacturing

224

67

42

34

40

—

—

10

2

—

—

2

420

Steel Product Manufacturing from Purchased
Steel

101

129

23

5

16

83

6

4

2

7

1

1

378

Other General Purpose Machinery
Manufacturing

159

37

78

15

53

4

9

14

4

—

—

4

377

Audio and Video Equipment Manufacturing

83

43

88

42

33

8

27

18

3

8

7

1

361

Medical Equipment and Supplies Manufacturing 119

42

86

25

50

3

7

17

6

—

1

2

357

Household and Institutional Furniture andKitchen Cabinet Manufacturing

170

13

70

6

58

1

—

12

4

—

2

4

339

Forging and Stamping

148

51

53

11

33

13

12

11

—

—

1

2

336

Lime and Gypsum Product Manufacturing

168

80

20

12

16

6

7

5

3

—

1

1

318

Animal Food Manufacturing

134

13

26

82

23

1

6

6

10

7

1

3

313

Subsector

272This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Process Heating (GWh)

Facility HVAC (GWh)

Process Cooling And
Refrigeration (GWh)

Facility Lighting (GWh)Electro Chemical
Processes (GWh)Other Facility Support(GWh)

Conventional Boiler Use(GWh)

End Use Not Reported(GWh)

Other Nonprocess Use(GWh)

Onsite Transportation(GWh)

Total (GWh)Other Process Use (GWh)

Machine Drive (GWh)

Seafood Product Preparation and Packaging

129

13

25

79

22

1

5

6

9

7

1

3

301

Other Electrical Equipment and Component
Manufacturing

84

69

50

19

29

23

11

7

1

—

—

2

297

Industrial Machinery Manufacturing

125

29

60

12

41

3

7

11

3

—

—

3

294

Commercial and Service Industry Machinery
Manufacturing

124

29

60

12

41

3

7

11

3

—

—

3

292

Sugar and Confectionery Product
Manufacturing

124

12

24

76

21

1

5

6

9

6

1

3

289

Clay Product and Refractory Manufacturing

149

71

18

11

14

5

6

4

3

—

0

1

284

Ship and Boat Building

97

28

49

13

33

2

13

10

2

4

3

4

258

Motor Vehicle Manufacturing

90

26

45

12

30

2

12

9

2

4

3

3

237

Motor Vehicle Body and Trailer Manufacturing

73

21

36

10

24

1

9

8

2

3

2

3

191

Machine Shops; Turned Product; and Screw,
Nut, and Bolt Manufacturing

79

27

29

6

18

7

7

6

—

—

0

1

181

Other Transportation Equipment Manufacturing 60

17

30

8

20

1

8

6

1

2

2

2

158

Metalworking Machinery Manufacturing

66

15

32

6

22

2

4

6

2

—

—

2

157

Agriculture, Construction, and Mining Machinery
58
Manufacturing

13

28

5

19

1

3

5

1

—

—

1

135

Spring and Wire Product Manufacturing

59

20

21

4

13

5

5

5

—

—

0

1

134

Electrical Equipment Manufacturing

35

29

21

8

13

10

5

3

1

—

—

1

126

Other Textile Product Mills

52

7

13

7

10

0

0

2

5

—

—

2

99

Boiler, Tank, and Shipping Container
Manufacturing

40

14

14

3

9

3

3

3

—

—

0

1

90

Subsector

273
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Conventional Boiler Use(GWh)

End Use Not Reported(GWh)

Other Nonprocess Use(GWh)

Onsite Transportation
(GWh)

7

0

2

1

2

—

—

1

89

Sawmills and Wood Preservation

64

5

6

1

5

0

1

1

2

1

0

0

87

Office Furniture (including Fixtures)
Manufacturing

41

3

17

1

14

0

—

3

1

—

0

1

82

Cut and Sew Apparel Manufacturing

26

3

18

1

11

—

—

2

1

—

—

0

63

Electric Lighting Equipment Manufacturing

17

14

10

4

6

5

2

1

0

—

—

0

61

Engine, Turbine, and Power Transmission
Equipment Manufacturing

25

6

12

2

8

1

1

2

1

—

—

1

59

Fabric Mills

33

5

8

2

4

0

1

1

1

—

—

0

56

Ventilation, Heating, Air-Conditioning, andCommercial Refrigeration Equipment
Manufacturing

23

5

11

2

8

1

1

2

1

—

—

1

54

Fiber, Yarn, and Thread Mills

26

4

6

2

3

0

1

1

1

—

—

0

43

Household Appliance Manufacturing

12

10

7

3

4

3

2

1

0

—

—

0

43

Cutlery and Handtool Manufacturing

17

6

6

1

4

2

1

1

—

—

0

0

38

Other Furniture Related Product Manufacturing

14

1

6

0

5

0

—

1

0

—

0

0

28

Tobacco Manufacturing

6

1

2

3

1

0

0

1

0

1

0

0

15

Textile Furnishings Mills

4

1

1

1

1

0

0

0

0

—

—

0

8

Hardware Manufacturing

4

1

1

0

1

0

0

0

—

—

0

0

8

Other Leather and Allied Product Manufacturing 4

1

1

0

1

0

0

0

0

—

—

0

7

Footwear Manufacturing

3

0

0

0

0

0

0

0

0

—

—

0

4

Railroad Rolling Stock Manufacturing

1

0

1

0

0

0

0

0

0

0

0

0

3

Machine Drive (GWh)274This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Total (GWh)Other Facility Support(GWh)

Other Process Use (GWh)3Electro Chemical
Processes (GWh)

Process Cooling And
Refrigeration (GWh)

13

Facility Lighting (GWh)

Facility HVAC (GWh)8Process Heating (GWh)

Textile and Fabric Finishing and Fabric Coating
53
Mills

Subsector

Other Facility Support(GWh)
Conventional Boiler Use(GWh)
End Use Not Reported(GWh)
Other Nonprocess Use(GWh)
Onsite Transportation(GWh)

Apparel Accessories and Other Apparel
Manufacturing
0
0
0
0
0
—
—
0
0
—
—
0
1

Leather and Hide Tanning and Finishing
0
0
0
0
0
0
0
0
0
—
—
0
0

Total
40,613 9,504
9,040
6,821
5,648
4,194
2,276
1,917
1,340
1,168
411
385

275

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Total (GWh)Other Process Use (GWh)Electro Chemical
Processes (GWh)

Facility Lighting (GWh)

Process Cooling And
Refrigeration (GWh)

Facility HVAC (GWh)

Process Heating (GWh)

Machine Drive (GWh)

Subsector

83,319Figure G-62. Industrial electricity use diurnal patterns by season, modeled for the Pacificcensus division 2012

276
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Table G-64. Distributed Generation Model, Annual Summary the Pacific Census Division in 2012by Sector and DG Type
Sector/DG TypeCHP (GWh)

Industry

Commercial

Residential

Total

27,8165,508

533,330Distributed PV (GWh)

683

681

1,385

2,749

Thermal DG (GWh)

1,493

288

—

1,780

Total (GWh)

29,992

6,477

1,391

37,859CHP (%)

92.7

85.0

0.4

88.0

Distributed PV (%)

2.3

10.5

99.6

7.3

Thermal DG (%)

5.0

4.4

—

4.7

Total (%)

79.2

17.1

3.7

100.0

Figure G-63. Distributed Generation Model Diurnal Generation Patterns, Pacificcensus division 2012

277
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Appendix H. Weather File Methodology

Many building energy analyses rely on typical meteorological year (TMY) weather files tocapture expected energy efficiency impacts over long time horizons. Building energy model
calibration requires actual meteorological year (AMY) weather files matched to the period forwhich measured data are available (Hale et al. 2014), but otherwise, analysis with AMY weather
files is relatively uncommon.
For this project, to produce load data that are usable in grid models with significant penetrationsof wind and solar generation, we require AMY weather files for an appropriate historical year.
These files were generated by transforming data from the National Solar Radiation Database(NSRDB) 62 into the format and units required by EnergyPlus weather (EPW) files (Wilcox 2012;
DOE 2017c) (Figure H-1).

Figure H-1. Example of alignment of integrated AMY NSRDB solar radiation profiles with EPW time
conventions as demonstrated using a TMY EPW file

NSRDB data are available at half-hourly resolution and represent an instantaneous measurement.
In contrast, EPW files are typically hourly, with an end-of-period interpretation for data points.
Thus, we apply trapezoidal rule averaging and integrating methods that use three NSRDB data
points to construct every EPW data point. With t representing the time for which we areconstructing an EPW data point these methods are:
Average:
EPW(𝑡𝑡) = 0.25 ∙ NSRDB(𝑡𝑡 − 1h) + 0.5 ∙ NSRDB(𝑡𝑡 − 0.5h) + 0.25 ∙ NSRDB(𝑡𝑡)Integrate:

62

EPW(𝑡𝑡) = 0.25h ∙ NSRDB(𝑡𝑡 − 1h) + 0.5h ∙ NSRDB(𝑡𝑡 − 0.5h) + 0.25h ∙ NSRDB(𝑡𝑡)

See https://nsrdb.nrel.gov/.

278
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The field mappings of EPW and NSRDB, along with the methods used to convert between the
two are summarized in Table H-1.
Table H-1. Mapping to EnergyPlus Weather File Fields from NSRDB Fields
EPW

NSRDB

Field

Units

Field

Units

Conversion
FactorSummary
MethodRelative Humidity%

Relative Humidity%-

AverageSnow Depthcm

Snow Depthm

100 cm/m

Average

Dew Point
Temperature°C

Dew Point
Temperature

K°C = K – 273.15

Average

Surface Pressure

Pa

Surface Pressurembar

100 Pa/mbar

AverageDry Bulb Temperature°C

Surface Temperature

K°C = K – 273.15

Average

Precipitable Watermm

Total Precipitable
Water

mm

-

AverageWind Speedm/sWind Speedm/s-

AverageWind Direction°

Wind Direction°-

Average

Global Horizontal
Radiation (GHR)

Wh/m2

Global Horizontal
Irradiance (GHI)

W/m2

-

Integrate

Direct Normal
Radiation (DNR)

Wh/m2

Direct Normal
Irradiance (DNI)

W/m2

-

Integrate

Diffuse Horizontal
Radiation (DHR)

Wh/m2

Diffuse Horizontal
Irradiance (DHI)

W/m2

-

Integrate

In addition to handling unit and data point interpretation conversions, the weather file timeextents were made to match the needs of producing a data set that covers 2012 for the entire
CONUS, using EST as our basis for defining the exact extents of the year. To ensure fullcoverage, an additional day of simulation—December 31, 2011 local time—was added. The
EnergyPlus output timeseries were subsequently processed to drop hours falling outside
2012, EST.

279
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Appendix I. Combined Heat and Power andDistributed Thermal Model

To develop an estimate of hourly generation from combined heat and power (CHP) and other
distributed thermal generators, we start by merging EIA data available at the plant and unit level.
EIA Form 860 provides unit level prime mover and capacity information, with nameplate,summer, and winter capacity specified in MW (EIA 2013a). We aggregate this data to the plantprime mover level, and then join it with the EIA Form 923 generation and fuel data after
aggregating the latter over fuel type. At the end of this process, we have capacity and generation
data for each plant-prime mover combination, along with the code for the primary fuel usedduring the year. EIA Form 923 also provides information on sector—both a coarse specification
and a NAICS code—as well as whether the plant provides CHP services, name and location
information (EIA 2018, 923).
The source and disposition table of EIA Form 923 provides further details by plant that let us
estimate how much generation is used behind-the-meter. Quantities of electricity generated onsite and imported from the grid are reported: the sum is the total source electricity. The
disposition of that electricity is then reported in terms of station use, direct use on site (this iswhat we interpret as the behind-the-meter use of electricity), sales (retail and for resale), tolling
agreements and other outgoing electricity (EIA 2018). From these data, we estimate the behindthe-meter fraction as
𝑓𝑓behind-the-meter =Direct Use (MWh)Direct Use (MWh)=.
Net Source (MWh) Total Source (MWh) − Station Use (MWh)At this point we filter the plants down to just those that are non-utility CHP and other thermal
plants. The filters we apply are:•Sector – Plants that are commercial or industrial, CHP and not-CHP•

Prime mover – Other, hydro, wind, and PV are removed. All other prime movers areclassified as CHP/Thermal DG. 63 PV is covered by the distributed PV model. The othercategories we remove are of potential interest but are excluded because of their small
capacity and less available data (e.g., at the hourly level). As shown in Table I-1, this is
not a severe limitation, as 98% of the generation reported falls in our aggregated
CHP/Thermal DG category.

•Capacity and net generation – Some plants are reported as having zero capacity. These
are removed, as are plants that report negative net generation for those prime movers
classified as CHP/Thermal DG. However, we retain plants with zero net generation torepresent capacity that is available but not used on a regular basis.

Following the application of the filters, we have data on 847 plants. Next, we determine forwhich of these plants a CEMS hourly profile is available. This join is done based on the Plant ID
and yields 69 plants with a CEMS profile; however, only 23 of those have a non-zero profile. For
The EIA prime mover codes included in our CHP/distributed thermal model are: CA, CS, CT (Combined Cycle),
FC (Fuel Cell), GT (Combustion (Gas) Turbine, including Jet Engines), IC (Internal Combustion Engine), and ST(Steam Turbine).
63

280This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

each of those plants we calculate the capacity factor of the profile in two ways. The “standard
capacity factor” is calculated by dividing the annual generation represented in the profile by the
profile’s maximum value, that is, by assuming the plant’s capacity is equal to the maximum
output achieved in the annual profile. The “alternate capacity factor” is computed by dividing theannual generation in the CEMS profile by the plant capacity reported on EIA Form 860. Both ofthese capacity factors are retained and used in the process of matching modeled plants to CEMS
profiles and then transforming those profiles to match the capacity factors prescribed for eachmodeled plant.
Table I-1. Commercial and Industrial Power Plant Capacity and Generation by Prime Mover,per EIA Forms 860 and 923
Generator
Type

Nameplate
Capacity (GW)Net
Generation (TWh)Generation(%)

CHP/Thermal DG

34.52

154.12

98%

Hydro

0.69

2.37

2%

Other

0.11

0.31

0%

PV

0.12

0.14

0%

Wind

0.04

0.07

0%

Total

35.48

157.02

100%

The DOE Combined Heat and Power Installation Database (ICF Inc. and DOE 2016) lists CHP
capacity in kW by city, state, facility name, application, NAICS code, operational year, prime
mover, and fuel. For our purposes, we filter out CHP facilities for whom the application is“utilities,” and remove units installed after 2012. We also map NAICS codes to sector:
residential, commercial, or industrial. At the end of this process, we have information on 3,687
units in place as of 2012. They are fairly evenly split between small (< 1 MW and therefore noteligible for EIA reporting) and large (1 MW or greater) units, coming in at 1,951 units and 1,736
units respectively. In principle, one might expect the number of large CHP units in the CHP DB
to be the same as the number of CHP units reported in the EIA data sources explained above.
However, the EIA data sources only report 687 CHP plants, that is, 40% of the expected 1,736.
The difference between data sources similarly holds if we look at capacity instead of number of
installations (Table I-2). Therefore, we model CHP capacity from the CHP DB, and distributed
thermal generation units reported in the EIA data sources (as such units are not listed in the CHP
DB).
Table I-2. CHP and Thermal DG Capacity Described in EIA Forms 860 and 923 (EIA) and in the DOE
Combined Heat and Power Installation Database (CHP DB)aCapacity Type

Commercial (MW)Industrial (MW)Residential (MW)

Total (MW)EIA DG

781

2,499

—

3,280

EIA CHP

2,542

28,032

44

30,618CHP DB >= 1 MW

9,330

69,08897

78,515

CHP DB < 1 MW

344

137

29

510

a Capacity included in the CHP/Thermal DG model is shaded orange.

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We assign behind-the-meter generation quantities to the CHP DB entries by performing a match
between the CHP DB and EIA meta-data. In particular, we attempt to match operational data to
CHP DB capacity on the basis of•

Sector information – First by Residential, Commercial, Industrial, then by increasingly
specific NAICS codes (starting with three-digit and progressing up to five-digit if
possible)•Prime mover and fuel type – We first attempt to match just on prime mover. If that is
successful and there are still multiple potential matches, we then attempt to match on
prime mover-fuel type pairs. In both cases, the mapping that is applied between EIA
codes and the CHP DB designations are listed in Table I-3.

•

Capacity – For CHP DB plants of 1 MW or larger, we filter EIA plants based on
capacity, for tighter and tighter bounds. For each run of the filter, the EIA plants that are
kept are those whose capacities are between 𝑓𝑓 ∙ 𝐶𝐶CHP DB and (1⁄𝑓𝑓 ) ∙ 𝐶𝐶CHP DB , where f is afraction that starts at 0.5 and can go as high as 0.99, and 𝐶𝐶CHP DB is the capacity of theplant for which we are defining operational data.

•

Location – The only location filter applied is state

These filters are applied in a particular order, starting with those aspects that we are mostinterested in matching, and proceeding to match at finer and finer levels as long as multiple
potential EIA matches remain (Algorithm 1).
Table I-3. Mapping Between EIA and CHP DB Prime Mover and Fuel Type Codesa
EIA

CHP DB

Prime Mover Code

Fuel Type CodePrime Mover

Fuel Class

IC

DFO

Reciprocating Engine

OIL

GT

DFO

Combustion Turbine

OIL

ST

SUB

Boiler/Steam Turbine

COAL

CA

NG

Combined Cycle

NG

GT

NG

Combustion Turbine

NG

ST

AB

Boiler/Steam Turbine

BIOMASSST

BIT

Boiler/Steam Turbine

COAL

ST

BLQ

Boiler/Steam Turbine

WAST

ST

DFO

Boiler/Steam Turbine

OIL

ST

NG

Boiler/Steam Turbine

NG

IC

NG

Reciprocating Engine

NG

ST

WDS

Boiler/Steam Turbine

WOOD

CS

NG

Combined Cycle

NG

CA

LFG

Combined Cycle

BIOMASSIC

OBG

Reciprocating Engine

BIOMASSST

PUR

Boiler/Steam Turbine

WAST

282
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EIA

CHP DB

Prime Mover Code

Fuel Type CodePrime Mover

Fuel ClassST

OG

Boiler/Steam Turbine

OTR

ST

OTH

Boiler/Steam Turbine

OTR

CT

NG

Combined Cycle

NG

GT

JF

Combustion Turbine

OIL

CA

DFO

Combined Cycle

OIL

ST

WH

Boiler/Steam Turbine

WAST

GT

OG

Combustion Turbine

OTR

ST

BFG

Boiler/Steam Turbine

WAST

ST

MSB

Boiler/Steam Turbine

BIOMASSST

LIG

Boiler/Steam Turbine

COAL

ST

RFO

Boiler/Steam Turbine

OIL

CA

BIT

Combined Cycle

COAL

CA

WH

Combined Cycle

WAST

GT

BIT

Combustion Turbine

COAL

All acronyms are defined in the acronyms list on page vi.

Algorithm 1—Match EIA Operational Data to CHP DB Instances
Input: CHP DB Instance to Match,
Set of 687 CHP Plants with EIA Data
No additional screens are applied as soon as the filtered subset is down to 1.
1: Screen on 3-digit NAICS
2: If no matches:3:
Screen on sector (Residential, Commercial, Industrial)
4: Screen on prime mover
5: If prime mover matched:
6:
Screen on prime mover and fuel type
7: If CHP DB Capacity 1 MW or greater:
8:
Screen capacity with f = 0.5
9: Screen on 4-digit NAICS
10: Screen on state
11: Screen on 5-digit NAICS
12: While f < 0.99:
13:
Screen on capacity with f = 1 – (1 – f)/2
Output: Subset of EIA CHP Plants that Match the CHP DB Instance283
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The matching granularity varies quite a bit across instances. Only 3% of instances achieved
matches at the closest level possible: a sectoral match at the five-digit NAICS code level, andalso matched on prime mover and fuel, state, and capacity (with f ≥ 0.5). Plants achieving thatlevel of match are generally associated with the most common large-scale CHP applications:pulp and paper, wastewater treatment, refining, chemicals, food processing, and primary metals.
Even so, those plants often ended up matched to a few or a handful of EIA plants, rather than asingle one. In contrast, 1,369 of the 3,687 CHP DB plants considered did end up with a single
match. With only one residential CHP plant in the EIA data, all 233 instances of residential CHP
listed in the CHP DB were mapped to that instance. In contrast, getting down to a single match
happened in many different ways for commercial and industrial plants. In some cases, there wasonly one EIA plant matching at the three-digit NAICS code level. In others the state was also
matched, or the prime mover and fuel, or just the prime mover. The types of match achieved foreach filter category are summarized in Table I-4. Once the final set of matching EIA CHP plants
was returned for a given CHP DB plant, a single one was selected at random and the CHP DB
plant’s capacity factor and behind the meter fraction assigned accordingly.
Table I-4. Results of Matching CHP Plants in the DOE CHP DB with CHP Plant Reports in 2012 EIA
Form 860 and Form 923 Data
Prime
Sector No. Plants Mover
Match Plants (%) Match

No. Plants Size
Plants (%) Match

No. Plants Location No. Plants
Plants (%) Match
Plants (%)

Sector

1,124

30 None1,055

29 None2,403

65 None

1,942

53

NAICS
threedigit

1,875

51

Prime
Mover

431

12 0.5684

19 State

1,745

47

NAICS
fourdigit

235

Prime
6 Mover &
Fuel

2,201

60 0.75

299

8

NAICS
fivedigit

453

12

0.875

154

4

> 0.875

147

4

Similar, but abbreviated, matching processes were then applied to select plants with CEMS datafrom which to derive an hourly generation profile. These processes were slightly different
depending on whether the plant being matched was a CHP DB plant, or a distributed thermal
plant from the EIA data. The two matching processes used are summarized in Algorithm 2 andAlgorithm 3 respectively. In both cases, the final profile is chosen by comparing the capacity
factor of the plant being matched to the two capacity factors associated with the CEMS profile.
Both the chosen CEMS Plant Id, and which CEMS capacity factor (standard or alternate) should
be used as a starting point for generating the modeled plant’s profile, are noted.

284This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Algorithm 2—Match CEMS Profiles to CHP DB Instances
Input: CHP DB Instance to Match,
Set of 23 Plants with non-zero CEMS Data
No additional screens are applied as soon as the filtered subset is down to 1.
1: Screen on EIA Operational Match is one of the 23 CEMS Plants
2: Screen on 3-digit NAICS
3: If no matches:
4:
Screen on sector (Residential, Commercial, Industrial)
4: Screen on prime mover
5: If prime mover matched:
6:
Screen on prime mover and fuel typeOutput: Subset of CEMS Plants that Match the CHP DB Instance
Algorithm 3—Match CEMS Profiles to EIA Distributed Thermal Instances
Input: EIA Distributed Thermal Instance to Match,
Set of 23 Plants with non-zero CEMS Data
No additional screens are applied as soon as the filtered subset is down to 1.
1: Screen on this plant is one of the 23 CEMS Plants
2: Screen on 2-digit NAICS
3: If no matches:
4:
Screen on sector (Residential, Commercial, Industrial)
5: Screen on 3-digit NAICS
4: Screen on prime mover
Output: Subset of CEMS Plants that Match the EIA Distributed Thermal
Instance

Finally, a profile for each modeled plant is created by transforming the selected CEMS plant
profile to match the modeled plant’s capacity factor and then multiplying the result by thebehind-the-meter fraction. The CEMS profile transformation proceeds by representing the profile
in terms of hourly capacity factors, finding the minimum generation point of the profile (theminimum over all hours when generation is non-zero), and then decomposing the profile into abaseload and non-baseload part. The non-baseload part is equal to the original profile minus theminimum generation point, truncated to be non-negative. This is retained as the “shape” of the
profile. We then compute a new minimum generation point such that that level of generation plus
the “shape” results in the desired annual capacity factor. And that is the returned profile if thedesired capacity factor is large enough. If the desired capacity factor is smaller than that achievedby retaining only the “shape” of the profile, we instead multiply the original profile by a simple
scaling factor (that is necessarily less than one).
The CEMS profiles that were used to feed this process are shown in Figure 15. The resulting
aggregated CHP and distributed thermal profiles are depicted in Figure F-4 for all of the
CONUS. Census division level plots are available in Appendix G.

285This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Appendix J. Raw State-Level System Loss Factors

Table J-1 provides the raw state-level data referenced and plotted in Section 2.3.4.1. Large
differences between the so-called Reported and Estimated loss factors indicate somecombination of discrepancies in data reported to EIA and FERC, or in our allocation of hourly
load data from planning regions, to transmission nodes, and then up to the state level. For one
example of the latter type of discrepancy, any load that is metered in a different state than theassociated transmission node is almost certain to have been miscounted, that is, assigned to thestate in which the transmission node resides rather than the state in which the meter resides.
Table J-1. Raw State-Level Loss Factors (a) Based on EIA Form 861 Data Alone (Reported),(b) Based on Comparing FERC Form 714 and EIA Form 861 Data (Estimated), and (c) Theirdifference (Estimated – Reported)State

EIA Annual
Site
EIA Annual Reported
EnergyLossesData Loss
(TWh)
(TWh)
Factor (%)

FERC
HourlyLoad(TWh)

FERC –EIA Loss
Estimate(TWh)

Estimated EstimatedLoss– Reported
Factor (%)(% pt)

RI

7.7

0.4

5.0

4.9

-2.8

-36.2

-41.1

DC

11.4

1.3

11.2

8.0

-3.4

-29.6

-40.8

WY

17.1

0.3

1.8

11.7

-5.4

-31.5

-33.3

IN

105.3

3.4

3.2

80.4

-24.9

-23.6

-26.8

VT

5.5

0.4

6.6

5.1

-0.4

-7.8

-14.4

CO

57.4

4.3

7.5

56.7

-0.7

-1.2

-8.7

KY

89.1

3.6

4.1

86.1

-3.1

-3.5

-7.6

NM

23.2

1.2

5.4

22.7

-0.5

-2.1

-7.5

FL

221.6

14.0

6.3

220.1

-1.5

-0.7

-7.0

AR

47.0

3.5

7.4

47.7

0.7

1.6

-5.8

UT

29.8

0.3

0.9

28.4

-1.4

-4.7

-5.6

GA

131.3

8.8

6.7

133.0

1.7

1.3

-5.4

NE

30.9

2.3

7.5

31.7

0.8

2.5

-5.0

DE

11.6

0.8

6.9

11.9

0.3

2.2

-4.7

WA

94.2

4.4

4.6

94.3

0.1

0.1

-4.5

CA

269.0

12.9

4.8

271.5

2.5

0.9

-3.9

OR

48.1

6.3

13.2

52.9

4.8

9.9

-3.3

KS

40.5

2.8

6.9

42.1

1.6

4.1

-2.9

ME

11.7

0.1

1.2

11.5

-0.1

-0.9

-2.2

MI

105.0

5.6

5.4

109.4

4.3

4.1

-1.2

OH

153.0

13.1

8.5

164.5

11.5

7.5

-1.0

AL

86.3

4.6

5.3

90.2

3.8

4.4

-0.9

VA

108.0

3.5

3.3

111.1

3.1

2.9

-0.4

LA

84.9

4.0

4.7

88.8

3.9

4.6

-0.1

286
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StateEIA Annual
Site
EIA Annual Reported
EnergyLossesData Loss
(TWh)
(TWh)
Factor (%)

FERC
HourlyLoad(TWh)

FERC –EIA Loss
Estimate(TWh)

Estimated EstimatedLoss– Reported
Factor (%)(% pt)

PA

144.9

7.7

5.3

152.6

7.7

5.3

0.0

NC

128.4

9.4

7.3

138.0

9.6

7.5

0.1

OK

59.9

5.6

9.4

65.8

5.8

9.8

0.4

SC

78.1

3.0

3.8

81.6

3.6

4.6

0.8

TN

96.5

6.5

6.8

103.9

7.4

7.7

0.9

TX

366.2

19.4

5.3

388.8

22.6

6.2

0.9

NJ

75.1

3.1

4.2

79.2

4.2

5.5

1.4

CT

29.5

0.8

2.7

30.9

1.4

4.7

2.1

MS

48.5

2.9

6.0

52.7

4.2

8.7

2.7

MN

68.3

4.3

6.3

75.5

7.3

10.7

4.4

AZ

75.2

4.8

6.4

83.7

8.6

11.4

5.0

WI

69.0

2.3

3.3

75.2

6.2

8.9

5.6

NV

35.3

1.0

2.9

38.3

3.0

8.5

5.7

IL

144.1

3.6

2.5

156.0

11.9

8.2

5.8

NY

147.2

5.8

3.9

163.1

15.9

10.8

6.9

MA

55.7

1.3

2.3

61.1

5.3

9.6

7.3

MD

61.9

2.3

3.8

69.9

8.0

13.0

9.2

NH

10.9

0.5

4.6

12.5

1.6

14.7

10.1

ID

23.7

1.5

6.1

28.5

4.8

20.1

13.9

ND

14.8

1.8

12.0

19.3

4.5

30.6

18.6

SD

11.9

0.5

3.8

14.7

2.8

23.4

19.6

WV

30.8

0.0

0.0

37.1

6.2

20.3

20.2

MO

82.7

6.2

7.5

107.5

24.9

30.1

22.6

MT

13.9

0.6

4.6

19.5

5.6

40.5

35.9

IA

45.8

1.8

3.9

69.7

23.9

52.1

48.2

287
This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

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NREL/TP-6A20-71492 • August 2018
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